├── .github └── ISSUE_TEMPLATE │ ├── bug_report.md │ ├── feature_request.md │ └── question_or_discussion.md ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── FinanceSchemes.md ├── LICENSE ├── OtherSuccessStories.md ├── PULL_REQUEST_TEMPLATE.md ├── README.md ├── ReadMore.md ├── SECURITY.md └── Sensors.md /.github/ISSUE_TEMPLATE/bug_report.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: 🐞 Bug Report 3 | about: Report a problem or malfunction you found in the project 4 | title: "[BUG] " 5 | labels: bug 6 | assignees: '' 7 | 8 | --- 9 | 10 | ## 🐛 Description 11 | 12 | A clear and concise description of the bug. 13 | 14 | --- 15 | 16 | ## ✅ Steps to Reproduce 17 | 18 | Please provide steps to reproduce the issue: 19 | 20 | 1. Go to '...' 21 | 2. Click on '...' 22 | 3. See error 23 | 24 | --- 25 | 26 | ## 📸 Screenshots / Logs (if applicable) 27 | 28 | Please attach screenshots or relevant logs that help explain the issue. 29 | 30 | --- 31 | 32 | ## 💻 Environment 33 | 34 | - Area Affected (Documentation / API / IoT / Other): 35 | - Device / OS / Platform: 36 | - Branch / Version: 37 | 38 | --- 39 | 40 | ## 🧾 Additional Context 41 | 42 | Any other information that may help us understand and resolve the issue. 43 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/feature_request.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: 💡 Feature Request 3 | about: Suggest a new feature, enhancement, or addition 4 | title: "[FEATURE] " 5 | labels: enhancement 6 | assignees: '' 7 | 8 | --- 9 | 10 | ## 💡 Proposed Feature 11 | 12 | Briefly describe the feature or enhancement you are suggesting. 13 | 14 | --- 15 | 16 | ## 🔍 Motivation 17 | 18 | Why is this feature important or beneficial for the project? 19 | 20 | --- 21 | 22 | ## 🛠 Technical or Functional Requirements (if any) 23 | 24 | If applicable, describe the expected design or flow: 25 | 26 | - 27 | - 28 | - 29 | 30 | --- 31 | 32 | ## 🤝 Collaboration 33 | 34 | Would you like to contribute this feature yourself? 35 | 36 | - [ ] Yes 37 | - [ ] No 38 | - [ ] I can help with testing or documentation 39 | 40 | --- 41 | 42 | ## 📄 Additional Notes 43 | 44 | Add any other information or references that support this request. 45 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/question_or_discussion.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: ❓ General Question / Discussion 3 | about: Ask a question, open a discussion, or share thoughts 4 | title: "[DISCUSSION] " 5 | labels: question 6 | assignees: '' 7 | 8 | --- 9 | 10 | ## ❓ Topic 11 | 12 | What would you like to ask, clarify, or discuss? 13 | 14 | --- 15 | 16 | ## 📌 Context 17 | 18 | Why is this topic important to bring up in this project? 19 | 20 | --- 21 | 22 | ## 🗨 Suggestions or Initial Thoughts 23 | 24 | Please share your ideas or initial thoughts if any. 25 | 26 | --- 27 | 28 | ## 🙋 Participation 29 | 30 | Would you be interested in working on this or helping coordinate? 31 | 32 | - [ ] Yes 33 | - [ ] No 34 | - [ ] Possibly, depending on scope 35 | 36 | --- 37 | 38 | ## 📄 References (if any) 39 | 40 | Links, research papers, or documents related to this topic. 41 | -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Contributor Covenant Code of Conduct 2 | 3 | ## Our Pledge 4 | 5 | We as members, contributors, and leaders pledge to make participation in our 6 | community a harassment-free experience for everyone, regardless of age, body 7 | size, visible or invisible disability, ethnicity, sex characteristics, gender 8 | identity and expression, level of experience, education, socio-economic status, 9 | nationality, personal appearance, race, religion, or sexual identity 10 | and orientation. 11 | 12 | We pledge to act and interact in ways that contribute to an open, welcoming, 13 | diverse, inclusive, and healthy community. 14 | 15 | ## Our Standards 16 | 17 | Examples of behavior that contributes to a positive environment for our 18 | community include: 19 | 20 | * Demonstrating empathy and kindness toward other people 21 | * Being respectful of differing opinions, viewpoints, and experiences 22 | * Giving and gracefully accepting constructive feedback 23 | * Accepting responsibility and apologizing to those affected by our mistakes, 24 | and learning from the experience 25 | * Focusing on what is best not just for us as individuals, but for the 26 | overall community 27 | 28 | Examples of unacceptable behavior include: 29 | 30 | * The use of sexualized language or imagery, and sexual attention or 31 | advances of any kind 32 | * Trolling, insulting or derogatory comments, and personal or political attacks 33 | * Public or private harassment 34 | * Publishing others' private information, such as a physical or email 35 | address, without their explicit permission 36 | * Other conduct which could reasonably be considered inappropriate in a 37 | professional setting 38 | 39 | ## Enforcement Responsibilities 40 | 41 | Community leaders are responsible for clarifying and enforcing our standards of 42 | acceptable behavior and will take appropriate and fair corrective action in 43 | response to any behavior that they deem inappropriate, threatening, offensive, 44 | or harmful. 45 | 46 | Community leaders have the right and responsibility to remove, edit, or reject 47 | comments, commits, code, wiki edits, issues, and other contributions that are 48 | not aligned to this Code of Conduct, and will communicate reasons for moderation 49 | decisions when appropriate. 50 | 51 | ## Scope 52 | 53 | This Code of Conduct applies within all community spaces, and also applies when 54 | an individual is officially representing the community in public spaces. 55 | Examples of representing our community include using an official e-mail address, 56 | posting via an official social media account, or acting as an appointed 57 | representative at an online or offline event. 58 | 59 | ## Enforcement 60 | 61 | Instances of abusive, harassing, or otherwise unacceptable behavior may be 62 | reported to the community leaders responsible for enforcement at 63 | damith@drklk.org. 64 | All complaints will be reviewed and investigated promptly and fairly. 65 | 66 | All community leaders are obligated to respect the privacy and security of the 67 | reporter of any incident. 68 | 69 | ## Enforcement Guidelines 70 | 71 | Community leaders will follow these Community Impact Guidelines in determining 72 | the consequences for any action they deem in violation of this Code of Conduct: 73 | 74 | ### 1. Correction 75 | 76 | **Community Impact**: Use of inappropriate language or other behavior deemed 77 | unprofessional or unwelcome in the community. 78 | 79 | **Consequence**: A private, written warning from community leaders, providing 80 | clarity around the nature of the violation and an explanation of why the 81 | behavior was inappropriate. A public apology may be requested. 82 | 83 | ### 2. Warning 84 | 85 | **Community Impact**: A violation through a single incident or series 86 | of actions. 87 | 88 | **Consequence**: A warning with consequences for continued behavior. No 89 | interaction with the people involved, including unsolicited interaction with 90 | those enforcing the Code of Conduct, for a specified period of time. This 91 | includes avoiding interactions in community spaces as well as external channels 92 | like social media. Violating these terms may lead to a temporary or 93 | permanent ban. 94 | 95 | ### 3. Temporary Ban 96 | 97 | **Community Impact**: A serious violation of community standards, including 98 | sustained inappropriate behavior. 99 | 100 | **Consequence**: A temporary ban from any sort of interaction or public 101 | communication with the community for a specified period of time. No public or 102 | private interaction with the people involved, including unsolicited interaction 103 | with those enforcing the Code of Conduct, is allowed during this period. 104 | Violating these terms may lead to a permanent ban. 105 | 106 | ### 4. Permanent Ban 107 | 108 | **Community Impact**: Demonstrating a pattern of violation of community 109 | standards, including sustained inappropriate behavior, harassment of an 110 | individual, or aggression toward or disparagement of classes of individuals. 111 | 112 | **Consequence**: A permanent ban from any sort of public interaction within 113 | the community. 114 | 115 | ## Attribution 116 | 117 | This Code of Conduct is adapted from the [Contributor Covenant][homepage], 118 | version 2.0, available at 119 | https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. 120 | 121 | Community Impact Guidelines were inspired by [Mozilla's code of conduct 122 | enforcement ladder](https://github.com/mozilla/diversity). 123 | 124 | [homepage]: https://www.contributor-covenant.org 125 | 126 | For answers to common questions about this code of conduct, see the FAQ at 127 | https://www.contributor-covenant.org/faq. Translations are available at 128 | https://www.contributor-covenant.org/translations. 129 | -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing to the Coconut Plantation Smart AgriTech Research Project 2 | 3 | First and foremost, thank you for showing interest in contributing. This project is part of a community-driven research effort aiming to improve the yield of coconut plantations in Sri Lanka through AgriTech and IT collaboration. 4 | 5 | Our goal is to create a **fully open-source, multilingual, and field-validated model** that benefits farmers, students, researchers, and policymakers alike. We believe that breaking down institutional silos and working together can help build a sustainable and inclusive future for agriculture. 6 | 7 | --- 8 | 9 | ## 🌱 How You Can Contribute 10 | 11 | We welcome contributions in the following areas: 12 | 13 | ### Research and Knowledge 14 | 15 | - Agricultural Scientists (especially in Coconut Yield, Soil Science, and Plantation Techniques) 16 | - AgriTech or IT Professors and PhD Researchers 17 | - DPI/DPG Advisors to guide us in aligning with Sri Lankan and international standards 18 | 19 | ### Technical and Engineering 20 | 21 | - IoT and Embedded Systems Engineers (Agri-focused device prototyping and data collection) 22 | - Software Engineers (API, backend, dashboards) 23 | - DevOps / Cloud Engineers (OCI, GitHub Actions, Infrastructure) 24 | - Data Scientists and Analysts (Yield modelling, forecasting, and reporting) 25 | - Translators (Sinhala, Tamil, English) 26 | - Technical Writers (Documenting project scope, research, and usage) 27 | 28 | ### Project and Community Roles 29 | 30 | - Volunteer Project Managers (with experience managing GitHub Projects) 31 | - GitHub Community Moderators (to help manage issue threads and pull requests) 32 | - Open Source Contributors (bug reports, suggestions, fixes, enhancements) 33 | - Legal / Ethical Advisors (data rights, licensing, research ethics) 34 | 35 | --- 36 | 37 | ## 🌍 Guiding Principles 38 | 39 | - **Openness**: All contributions must be shared openly and respectfully. 40 | - **Inclusiveness**: All backgrounds and skill levels are welcome. 41 | - **Local First**: Our focus is Sri Lankan agriculture. Solutions must be localised and practical. 42 | - **Documentation Matters**: Every contribution, whether code or research, must be well-documented in plain English. Sinhala and Tamil support is highly appreciated. 43 | 44 | --- 45 | 46 | ## 🛠 Getting Started 47 | 48 | 1. **Fork the repository** and clone it to your local machine. 49 | 2. Read the existing documentation and open issues. 50 | 3. Raise an issue if you would like to work on something new. 51 | 4. When ready, submit a pull request to the `develop` branch. 52 | 5. Clearly explain your changes, and tag relevant GitHub users using `@username` if needed. 53 | 54 | --- 55 | 56 | ## 📢 Communication 57 | 58 | We use GitHub Issues for tracking tasks and discussions. Larger decisions and roadmap topics will be discussed in GitHub Discussions or posted as project updates. 59 | 60 | We may also host regular community sync-ups depending on contributor availability and interest. 61 | 62 | --- 63 | 64 | ## 🙏 Final Note 65 | 66 | This project is run voluntarily and with great care. It is not backed by any organisation at this time. Your contribution will not only be technical or academic—it will have a **real-world impact** on communities and future farmers in Sri Lanka. 67 | 68 | Thank you again for being part of this journey. 69 | 70 | --- 71 | 72 | *Document last updated: {{21st April 2025}}* 73 | -------------------------------------------------------------------------------- /FinanceSchemes.md: -------------------------------------------------------------------------------- 1 | # Financial Support and Loan Schemes for Coconut Development in Sri Lanka 2 | 3 | Sri Lanka offers several financial assistance programs to support coconut cultivation and coconut-based industries. Key national schemes – from government bodies, development banks, and commercial banks (e.g. Bank of Ceylon) – are outlined below for easy comparison. Each scheme includes the provider, purpose, eligibility, loan terms, and application process. 4 | 5 | ## Kapruka Ayojana Loan Scheme (Coconut Cultivation Board + Partner Banks) 6 | 7 | - **Provider:** Coconut Cultivation Board (CCB) in partnership with banks such as People’s Bank, Bank of Ceylon, DFCC, SMIB, Seylan, Regional Development Bank, etc. ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=)) ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=The%20%E2%80%98Kapruka%20Ayojana%E2%80%99%20loan%20scheme,products%20in%20the%20world%20market)) 8 | 9 | - **Description:** A concessionary credit scheme to finance **coconut cultivation and farm development**. Loans are provided for new planting, replanting, intercropping in coconut lands, soil conservation, farm machinery, livestock integration (e.g. dairy cattle under coconut), and other improvements – aiming to develop coconut estates as integrated farms for higher productivity ([‘Kapruka Ayojana’ Credit Scheme (KACS) - DFCC Bank PLC](https://www.dfcc.lk/products/kapruka-ayojana-credit-scheme-kacs/#:~:text=%E2%80%98Kapruka%20Ayojana%E2%80%99%20credit%20scheme%20is,and%20equipment%20to%20further%20mechanization)) ([ 10 | Kapruka Loan | Banks in Sri Lanka | Commercial Banks in Sri Lanka 11 | ](https://www.seylan.lk/agriculture-loan/kapruka-loan#:~:text=Kapruka%20loan%20has%20been%20designed,to%20encourage%20domestic%20coconut%20production)). CCB also offers technical advisory support to borrowers under this scheme ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=)). 12 | 13 | - **Eligibility:** **Coconut land owners or long-term leaseholders** are eligible. The land should be between ½ acre and 50 acres ([‘Kapruka Ayojana’ Credit Scheme (KACS) - DFCC Bank PLC](https://www.dfcc.lk/products/kapruka-ayojana-credit-scheme-kacs/#:~:text=,Minimum%20equity%20contribution%20of%2020)). Lessees must have a lease at least 2 years beyond the loan term ([‘Kapruka Ayojana’ Credit Scheme (KACS) - DFCC Bank PLC](https://www.dfcc.lk/products/kapruka-ayojana-credit-scheme-kacs/#:~:text=,Minimum%20equity%20contribution%20of%2020)). Borrowers need to contribute a minimum of 15–20% equity of the project cost ([SME – Kapruka - Regional Development Bank](https://www.rdb.lk/loans-and-advance/sme-kapruka/#:~:text=Be%20a%20citizen%20of%20Sri,Have%20the%20effective%20repayment%20capacity)) ([‘Kapruka Ayojana’ Credit Scheme (KACS) - DFCC Bank PLC](https://www.dfcc.lk/products/kapruka-ayojana-credit-scheme-kacs/#:~:text=than%202%20years%20in%20excess,Minimum%20equity%20contribution%20of%2020)). They must not be loan defaulters and require a recommendation from the Coconut Cultivation Board confirming the viability of the proposed coconut development project ([SME – Kapruka - Regional Development Bank](https://www.rdb.lk/loans-and-advance/sme-kapruka/#:~:text=Be%20a%20citizen%20of%20Sri,Have%20the%20effective%20repayment%20capacity)). 14 | 15 | - **Loan Amount & Interest:** **Up to LKR 3–5 million per project is typical**, offered at a subsidized interest rate around **8% per annum** ([‘Kapruka Ayojana’ Credit Scheme (KACS) - DFCC Bank PLC](https://www.dfcc.lk/products/kapruka-ayojana-credit-scheme-kacs/#:~:text=,5%20Mn)). (The exact maximum can vary by bank – for instance, DFCC Bank offers up to Rs. 5 million ([‘Kapruka Ayojana’ Credit Scheme (KACS) - DFCC Bank PLC](https://www.dfcc.lk/products/kapruka-ayojana-credit-scheme-kacs/#:~:text=,5%20Mn)), Seylan Bank up to Rs. 3 million ([ 16 | Kapruka Loan | Banks in Sri Lanka | Commercial Banks in Sri Lanka 17 | ](https://www.seylan.lk/agriculture-loan/kapruka-loan#:~:text=Maximum%20Loan%20Quantum)), and some state banks even up to Rs. 10 million under this scheme ([State Mortgage & Investment Bank](https://www.smib.lk/en/sme/kapruka-ayojana-loans#:~:text=,8.00%25%20p.a)).) The interest rate is well below normal market rates, fixed in the single digits (e.g. 8% p.a. ([‘Kapruka Ayojana’ Credit Scheme (KACS) - DFCC Bank PLC](https://www.dfcc.lk/products/kapruka-ayojana-credit-scheme-kacs/#:~:text=,12%20months%20grace)) or 9% p.a. in some cases ([ 18 | Kapruka Loan | Banks in Sri Lanka | Commercial Banks in Sri Lanka 19 | ](https://www.seylan.lk/agriculture-loan/kapruka-loan#:~:text=,grace%20period%20of%2001%20year))) as it is partly subsidized by the government. 20 | 21 | - **Repayment Period:** Typically **5 years** total tenure, which **includes a grace period** on principal repayment of about **12–18 months** at the start ([‘Kapruka Ayojana’ Credit Scheme (KACS) - DFCC Bank PLC](https://www.dfcc.lk/products/kapruka-ayojana-credit-scheme-kacs/#:~:text=)) ([State Mortgage & Investment Bank](https://www.smib.lk/en/sme/kapruka-ayojana-loans#:~:text=,8.00%25%20p.a)). This allows coconut growers time for trees to start yielding. Borrowers start paying back after the initial grace (interest may be payable during grace as applicable) and can repay in installments over the remaining term. 22 | 23 | - **How to Apply:** Applications are made through participating banks. A prospective borrower should contact a bank offering the Kapruka Ayojana loan (for example, People’s Bank or Bank of Ceylon) and submit a project plan for coconut cultivation/development. The **Coconut Cultivation Board must evaluate and approve the project**, so the bank will forward the plan to CCB for recommendation ([SME – Kapruka - Regional Development Bank](https://www.rdb.lk/loans-and-advance/sme-kapruka/#:~:text=Be%20a%20citizen%20of%20Sri,Have%20the%20effective%20repayment%20capacity)). Once CCB approval is obtained, the bank processes the loan with its normal credit requirements. Borrowers will need to provide **acceptable security** – usually a mortgage of property or movable assets and/or guarantors as required by the bank ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=Securities)). Eligible farmers can contact local bank branches or CCB regional officers to initiate an application. (All loans are subject to the bank’s terms and CCB’s final approval of the project ([‘Kapruka Ayojana’ Credit Scheme (KACS) - DFCC Bank PLC](https://www.dfcc.lk/products/kapruka-ayojana-credit-scheme-kacs/#:~:text=,by%20the%20Coconut%20Cultivation%20Board)).) 24 | 25 | ## Kapruka Nipayum Diriya Industrial Loan Scheme (Coconut Dev. Authority + People’s Bank) 26 | 27 | - **Provider:** Coconut Development Authority (CDA) in collaboration with People’s Bank. (Launched under the Ministry of Plantation Industries in 2012 via an MoU with People’s Bank ([](https://parliament.lk/uploads/documents/paperspresented/1704951121068676.pdf#:~:text=,Bank%20in%20a%20Memorandum%20of)).) 28 | 29 | - **Description:** A **concessionary loan program for coconut-based industries**, intended to **boost coconut product manufacturing and processing businesses** ([Export of coconut related products increases - Sunday Observer](http://archives.sundayobserver.lk/2012/06/03/fin04.asp#:~:text=Observer%20archives,the%20industries%20related%20to%20coconut)). This scheme provides low-interest financing to encourage entrepreneurs to start or expand industries that add value to coconuts (for example, coconut oil mills, desiccated coconut processing, fiber and shell product manufacturing, etc.). It was introduced as “Kapruka Nipayum Diriya” (meaning “coconut industry strength”) to revitalize the coconut industry by supporting industrialists in the sector ([Export of coconut related products increases - Sunday Observer](http://archives.sundayobserver.lk/2012/06/03/fin04.asp#:~:text=Observer%20archives,the%20industries%20related%20to%20coconut)). 30 | 31 | - **Eligibility:** **Coconut industry entrepreneurs** – typically small and medium-scale industrialists engaged in processing or manufacturing coconut-based products. The business should be registered with the Coconut Development Authority to qualify. Applicants must meet People’s Bank’s credit criteria and obtain a recommendation from the CDA. In practice, the CDA coordinates to identify eligible industrialists (e.g. those who have registered coconut product factories) and recommends them for the loan ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=The%20Kapruka%20Jaya%20Isura%20loan,the%20recommendation%20of%20the%20authority)). This scheme does *not* cover cultivation; it is for value addition enterprises. 32 | 33 | - **Loan Amount & Interest:** Provides **mid-sized loans (approximately up to LKR 2 million)** to each eligible coconut industry project, on **highly concessionary interest terms**. The interest rate to the borrower is very low – around **6% per annum** effectively – because the Coconut Development Authority subsidizes a portion of the interest cost ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=Authority%20are%20eligible%20for%20the,the%20recommendation%20of%20the%20authority)). (People’s Bank initially extends the loan at its standard commercial rate, but the CDA reimburses a significant share of the interest to make it affordable ([](https://parliament.lk/uploads/documents/paperspresented/1704951121068676.pdf#:~:text=People%27s%20Bank%20%27Kapruka%20Nipayum%20Diriya%27,By%20paying%20the%20installments%20to)).) For working capital loans, the rate may be slightly higher (around 8%) but still subsidized ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=Authority%20are%20eligible%20for%20the,the%20recommendation%20of%20the%20authority)). This cheap credit is intended to reduce the financial burden on coconut product manufacturers. 34 | 35 | - **Repayment Period:** **Up to 5 years** repayment, typically in **60 monthly installments** ([](https://parliament.lk/uploads/documents/paperspresented/1704951121068676.pdf#:~:text=People%27s%20Bank%20%27Kapruka%20Nipayum%20Diriya%27,By%20paying%20the%20installments%20to)). (This corresponds to the term mentioned for People’s Bank’s implementation.) In some cases, longer terms were considered for certain investments, but generally the loan is amortized over about five years. Borrowers receive an interest rebate during the initial years: for example, the CDA fully covered the interest in the first year and 50% in the next few years in earlier implementations ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=capital%2C%20the%20interest%20rate%20is,repayment%20period%20is%20three%20years)). This effectively reduces the cost in the early stages of the project. The exact structure of interest subsidy/rebate may be subject to the agreement at loan signing. 36 | 37 | - **How to Apply:** Interested coconut product manufacturers can apply through **People’s Bank** under this scheme. Applications may be funneled through the CDA: typically, an entrepreneur should register with the Coconut Development Authority and submit their project proposal to the CDA or People’s Bank. The **CDA will verify the project’s viability and issue a recommendation** to the bank ([](https://parliament.lk/uploads/documents/paperspresented/1704951121068676.pdf#:~:text=,Bank%20in%20a%20Memorandum%20of)). With that, the applicant proceeds to People’s Bank which will handle the loan approval and disbursement. The loan is disbursed by People’s Bank, while the **CDA directly handles the interest subsidy**, reimbursing the agreed portion of interest to the borrower or the bank as per the scheme’s mechanism ([](https://parliament.lk/uploads/documents/paperspresented/1704951121068676.pdf#:~:text=People%27s%20Bank%20%27Kapruka%20Nipayum%20Diriya%27,By%20paying%20the%20installments%20to)). For more information, entrepreneurs can contact the Coconut Development Authority or People’s Bank enterprise credit division. 38 | 39 | ## Kapruka Jaya Isura Loan Scheme (Coconut Dev. Authority + Regional Dev. Bank) 40 | 41 | - **Provider:** Coconut Development Authority (CDA) in collaboration with Regional Development Bank (RDB – a state-owned development bank). This scheme was launched alongside Nipayum Diriya, via an agreement with RDB in 2017, targeting regions across Sri Lanka ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=The%20%E2%80%98Kapruka%20Ayojana%E2%80%99%20loan%20scheme,products%20in%20the%20world%20market)). 42 | 43 | - **Description:** A **nationwide concessionary loan program for coconut-based businesses**, similar in purpose to Nipayum Diriya, but implemented by RDB. It focuses on **financing coconut product industries (not cultivation)** – for example, producers of coconut oil, fiber, charcoal, coconut milk, and other value-added products ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=The%20Kapruka%20Jaya%20Isura%20loan,the%20recommendation%20of%20the%20authority)). The goal is to expand processing capacity and modernize coconut-related enterprises, thereby increasing the supply of coconut products for both domestic and export markets ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=In%20view%20of%20the%20current,some%20unique%20financial%20assistance%20schemes)) ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=The%20%E2%80%98Kapruka%20Ayojana%E2%80%99%20loan%20scheme,products%20in%20the%20world%20market)). By providing affordable credit, the scheme helps industrialists invest in equipment, technology, or working capital for coconut-related manufacturing. 44 | 45 | - **Eligibility:** **Entrepreneurs engaged in coconut allied products and industries** are eligible. Businesses must be **officially registered with the Coconut Development Authority**, and the **CDA must recommend the loan application** based on the project’s potential ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=The%20Kapruka%20Jaya%20Isura%20loan,the%20recommendation%20of%20the%20authority)). (The CDA ensures the applicant is a bona fide coconut industry player and that the project aligns with national coconut development goals.) Both new and existing coconut-based enterprises can apply, including those involved in traditional products (oil, desiccated coconut, coir, etc.) or innovative coconut derivative products. As with any loan, applicants should have a good credit record and repayment capacity as evaluated by RDB. 46 | 47 | - **Loan Amount & Interest:** **Loans up to LKR 2 million per project** are available ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=The%20Kapruka%20Jaya%20Isura%20loan,the%20recommendation%20of%20the%20authority)). The scheme offers a highly subsidized interest rate: **6% per annum for investment loans** (e.g. purchasing machinery or building facilities) and **8% per annum for working capital loans** ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=Authority%20are%20eligible%20for%20the,the%20recommendation%20of%20the%20authority)). These rates are fixed well below market rates. Moreover, to ease the burden, the **CDA provides an interest rebate**: 100% of the interest is reimbursed for the first year, and 50% of the interest from the second through fifth years is reimbursed to the borrower ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=capital%2C%20the%20interest%20rate%20is,repayment%20period%20is%20three%20years)). This effectively reduces the cost of borrowing substantially (in the first year the effective interest to the borrower is 0%, and in years 2–5 it is about 3% after rebate). After year five, the borrower pays the concessionary rate in full if the loan extends beyond that. 48 | 49 | - **Repayment Period:** The repayment terms depend on the loan purpose. **Investment loans** under Jaya Isura can be repaid over **up to 7 years** (to allow time for capital investments to generate returns) ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=Authority%20are%20eligible%20for%20the,the%20recommendation%20of%20the%20authority)). **Working capital loans** are shorter-term, typically to be repaid within **3 years** ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=Authority%20are%20eligible%20for%20the,the%20recommendation%20of%20the%20authority)). For both types, the possibility of a grace period or graduated payment structure exists, especially given the interest reimbursements in the early years. RDB usually structures the installment schedule such that the borrower benefits from the interest subsidy period. In summary, investment-oriented projects get a longer tenure, whereas loans for operational needs are shorter, matching the use of funds. 50 | 51 | - **How to Apply:** Applications are channeled through the **Regional Development Bank** branches across the country. An interested coconut industry owner should first engage with the Coconut Development Authority to obtain the necessary endorsements. The CDA will confirm the business is registered and the project falls under the scheme’s scope, then provide a recommendation letter for the loan. With this, the entrepreneur can apply at an RDB branch, submitting the business plan and financial statements as required. RDB will evaluate the creditworthiness and viability (often in consultation with CDA on technical aspects) ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=The%20Kapruka%20Jaya%20Isura%20loan,the%20recommendation%20of%20the%20authority)). Once approved, the loan is granted by RDB, and the **CDA oversees the interest subsidy payments** as per the scheme. This scheme is available **island-wide** via RDB’s network ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=The%20Kapruka%20Jaya%20Isura%20loan,the%20recommendation%20of%20the%20authority)). Prospective applicants can contact the nearest RDB branch or the CDA’s development division for guidance on the application process. 52 | 53 | ## Smallholder Agribusiness Partnership Programme (SAPP) 54 | 55 | - **Provider:** Ministry of Agriculture (Govt of Sri Lanka) with funding support from IFAD (International Fund for Agricultural Development) – implemented via participating banks (such as Bank of Ceylon, People’s Bank, etc.) under Central Bank oversight ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=,Partnership%20Programme)). 56 | 57 | - **Description:** SAPP is a **special credit and grant program to link small farmers with agribusiness value chains**. It operates on a **4P model** (Public-Private-Producer Partnerships), where a private sector buyer (e.g. an agribusiness or exporter) partners with groups of smallholder producers and a financing bank to develop a supply chain ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=,SAPP%20and%20tea%20%26%20rubber)). The program provides **concessionary loans, technical assistance, and matching grants** to support projects in various agriculture sectors – including coconut cultivation or processing – that will increase rural incomes. For example, a coconut-based project under SAPP might involve a company sourcing coconuts from small farmers who get funding to expand cultivation or invest in drip irrigation, with a purchase agreement in place. SAPP’s objective is to modernize and commercialize smallholder agriculture (covering crops, livestock, fisheries, etc.) by improving access to finance and markets ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=,Partnership%20Programme)) ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=Areas%20for%20which%20credit%20is,provided)). 58 | 59 | - **Eligibility:** **Smallholder farmers, farmer organizations, and agri-entrepreneurs** engaging in an approved 4P partnership are eligible. The target groups include: 60 | - Individual farmers or farmer groups cultivating crops or rearing livestock who have an agreement to supply a partner company ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=,SAPP%20and%20tea%20%26%20rubber)). 61 | - Producer organizations or cooperatives involved in agribusiness partnerships. 62 | - **Youth entrepreneurs (age 18–40)** starting agribusiness ventures in the agriculture value chain ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=,SAPP%20and%20tea%20%26%20rubber)). 63 | - **Private agribusiness companies (“Promoters”)** that partner with smallholders (these companies can also receive financing to on-lend to farmers or invest in processing, as part of the partnership) ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=,SAPP%20and%20tea%20%26%20rubber)). 64 | 65 | SAPP covers a wide range of agricultural activities – any commercially viable agribusiness proposal that involves small producers could be considered. For instance, coconut sector projects (e.g. a coconut milk processor working with village coconut growers, or a young entrepreneur making coconut-based snacks sourcing from local farms) would qualify. Participants must be willing to enter into the partnership agreement and meet the creditworthiness criteria of the banks. Typically, the program requires a form of buy-back agreement or off-take contract with the private partner to ensure market linkages for the farmers. 66 | 67 | - **Loan Amount & Interest:** **Concessionary interest rates** are offered, generally around **6.5% per annum** on loans under SAPP ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=Interest%20Rate)). The Central Bank provides refinance funds to banks to maintain these low rates (e.g. 3% refinance to banks, enabling final lending rates around 6.5%) ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=Interest%20Rate)). **Loan sizes vary based on the sub-scheme and borrower category**: 68 | - Small individual farmers can get smaller loans (e.g. up to around LKR 300,000 for minor investments) ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=,amount%20decide%20according%20to%20project)). 69 | - **Youth entrepreneurs** can borrow up to **LKR 2 million** to start agribusiness projects ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=,amount%20decide%20according%20to%20project)). 70 | - Larger producer organizations or **private promoters** facilitating a cluster of farmers can obtain bigger facilities (e.g. up to **LKR 18 million** for a promoter company) ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=,amount%20decide%20according%20to%20project)), or even more if justified by the project scale (some loans amounts are determined case-by-case for bigger value-chain projects ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=,amount%20decide%20according%20to%20project))). 71 | - These loans often come with an attached grant component – SAPP might provide a grant (e.g. 20-50% of the project cost) in addition to the loan, depending on the project’s nature and social benefits, though the exact grant percentage varies by scheme component. 72 | 73 | The concessional interest and grant support make financing under SAPP very affordable for farmers. This encourages adoption of new technology like irrigation for coconut smallholders or equipment for processing. 74 | 75 | - **Repayment Period:** **Up to 5 years repayment** is allowed, with **grace periods of 12 to 18 months** commonly included ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=Maximum%20Amount)). The exact term depends on the project cash flow. For instance, a loan for planting a perennial crop (like coconut or intercropping in a coconut garden) might include an 18-month grace until the first harvest, and then repayment over the next 3.5 years. Loans for equipment or livestock might have slightly shorter grace. Maximum tenure is usually around five years. In all cases, SAPP loans are structured to match the project’s income generation cycle and may offer flexible repayment (such as seasonal installments aligned with harvest). Because the program’s aim is to foster sustainable businesses, it avoids overly short repayment schedules that would strain smallholders. 76 | 77 | - **How to Apply:** SAPP is **project-oriented** – applications typically come through calls for proposals or via identified value-chain projects. A smallholder or group interested in SAPP should first be part of a 4P partnership proposal. Often an agribusiness company will initiate a project and sign up farmer suppliers who then apply for SAPP financing. However, individual entrepreneurs can also approach the SAPP coordinating unit (at the Ministry of Agriculture) or participating banks with a project idea. The steps generally are: 78 | 1. **Proposal Development:** Work with SAPP officials or an agribusiness partner to develop a bankable project plan (including the roles of the private partner, farmers, financing needs, etc.). 79 | 2. **Bank Application:** Submit the proposal to a participating bank (such as People’s Bank, Bank of Ceylon, or others involved in SAPP). The bank will evaluate the loan part of the project. 80 | 3. **Approval:** The project is reviewed by a SAPP committee and the bank. Once approved, the loan is disbursed by the bank, and any grant portion is provided through the SAPP program. 81 | 82 | Farmers’ organizations and companies can get information and help from the **SAPP Program Management Unit** under the Ministry or from the local agriculture extension offices. Because this is a national program, it operates in many districts – **all districts are covered** if viable projects are present. The key is to have an organized partnership; thus, direct walk-in applications may be guided to form or join a 4P arrangement. In summary, to access SAPP financing for a coconut-related venture, one should engage with the program facilitators (or an implementing partner like a processing company) and then apply through a partner bank with the project documentation ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=,SAPP%20and%20tea%20%26%20rubber)). 83 | 84 | --- 85 | 86 | **Sources:** Official bank brochures and websites, government announcements, and news reports have been used to compile the above information. For further details, see Coconut Cultivation Board and Coconut Development Authority publications and the specific bank links for each scheme ([‘Kapruka Ayojana’ Credit Scheme (KACS) - DFCC Bank PLC](https://www.dfcc.lk/products/kapruka-ayojana-credit-scheme-kacs/#:~:text=%E2%80%98Kapruka%20Ayojana%E2%80%99%20credit%20scheme%20is,and%20equipment%20to%20further%20mechanization)) ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=The%20Kapruka%20Jaya%20Isura%20loan,the%20recommendation%20of%20the%20authority)) ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=,Partnership%20Programme)), among others. These programs illustrate Sri Lanka’s commitment to supporting coconut growers and industries through affordable credit and are periodically updated by the authorities. Always refer to the latest guidelines from the relevant bank or agency when applying. 87 | 88 | 89 | # Comparison of Major Coconut Development Loan Schemes in Sri Lanka (as of April 2025) 90 | 91 | The table below compares key features of four major bank and government-supported loan schemes aimed at coconut cultivation and industry development in Sri Lanka. 92 | 93 | | **Bank** (Implementing Institution) | **Loan Scheme Name** | **Conditions (Eligibility & Use of Funds)** | **Maximum Loan Amount** | **Maximum Repayment (Years)** | **Interest Rate** | 94 | |-----------------------------------------------------------------------------|-----------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------|---------------------------------------------------|-------------------------------------------| 95 | | *Multiple banks*
(e.g., Bank of Ceylon, People’s Bank, DFCC Bank, etc., in partnership with Coconut Cultivation Board) | **Kapruka Ayojana** | Coconut cultivators (land owners or lessees, up to ~50 acres) for coconut **land development** – including new planting, replanting, intercropping, farm improvements (e.g. livestock integration) and machinery/equipment for cultivation ([‘Kapruka Ayojana’ Credit Scheme (KACS) - DFCC Bank PLC](https://www.dfcc.lk/products/kapruka-ayojana-credit-scheme-kacs/#:~:text=%E2%80%98Kapruka%20Ayojana%E2%80%99%20credit%20scheme%20is,and%20equipment%20to%20further%20mechanization)). Borrower must contribute at least 20% equity and have Coconut Cultivation Board (CCB) project approval. | Rs. 5 million ([‘Kapruka Ayojana’ Credit Scheme (KACS) - DFCC Bank PLC](https://www.dfcc.lk/products/kapruka-ayojana-credit-scheme-kacs/#:~:text=,5%20Mn)) *(final amount per project set by CCB)* | Up to 5 years (including ~1 year grace period) ([‘Kapruka Ayojana’ Credit Scheme (KACS) - DFCC Bank PLC](https://www.dfcc.lk/products/kapruka-ayojana-credit-scheme-kacs/#:~:text=,5%20Mn)) | 8% per annum (fixed concessionary rate) ([‘Kapruka Ayojana’ Credit Scheme (KACS) - DFCC Bank PLC](https://www.dfcc.lk/products/kapruka-ayojana-credit-scheme-kacs/#:~:text=,12%20months%20grace)) | 96 | | People’s Bank
(with Coconut Development Authority) | **Kapruka Nipayum Diriya** | Small and medium **coconut-based industrialists** (entrepreneurs in coconut product industries such as coir, coconut oil, fiber, etc.) to start or expand coconut processing/value-add businesses ([](https://parliament.lk/uploads/documents/paperspresented/1704951121068676.pdf#:~:text=Two%20low%20interest%20concessional%20loan,By%20paying%20the%20installments%20to)). Requires recommendation by the Coconut Development Authority (CDA). | Rs. 2 million ([](https://parliament.lk/uploads/documents/paperspresented/1704951121068676.pdf#:~:text=Also%2C%20according%20to%20the%20Memorandum,People%27s%20Bank%20has%20not%20yet)) | ~5 years (60 monthly installments) ([](https://parliament.lk/uploads/documents/paperspresented/1704951121068676.pdf#:~:text=People%27s%20Bank%20%27Kapruka%20Nipayum%20Diriya%27,interest)) | 6% per annum (effective, with interest subsidy by CDA) ([](https://parliament.lk/uploads/documents/paperspresented/1704951121068676.pdf#:~:text=People%27s%20Bank%20%27Kapruka%20Nipayum%20Diriya%27,interest)) | 97 | | Regional Development Bank (RDB)
(with Coconut Development Authority) | **Kapruka Jaya Isura** | **Coconut allied product industries** (e.g. traditional coconut oil production, coconut shell/fiber products, etc.) for business owners/SMEs engaged in coconut-related manufacturing ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=The%20Kapruka%20Jaya%20Isura%20loan,the%20recommendation%20of%20the%20authority)). Borrowers must be registered with CDA and obtain its recommendation; focuses on *non-cultivation* coconut sector development ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=The%20Kapruka%20Jaya%20Isura%20loan,the%20recommendation%20of%20the%20authority)). | Rs. 3.5 million ([](https://parliament.lk/uploads/documents/paperspresented/1704951121068676.pdf#:~:text=Also%2C%20according%20to%20the%20Memorandum,People%27s%20Bank%20has%20not%20yet)) *(raised from Rs. 2 Mn in 2020)* | Up to 7 years for investment loans (with ~1 year grace); up to 3 years for working capital loans ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=Under%20the%20loan%2C%20customers%20can,repayment%20period%20is%20three%20years)) | 6% p.a. (investment loans) or 8% p.a. (working capital) ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=Authority%20are%20eligible%20for%20the,the%20recommendation%20of%20the%20authority)) (interest subsidized 50% by CDA in initial years ([RDB Bank to offer relief for coconut cultivation and industry | Daily FT](https://www.ft.lk/Agriculture/RDB-Bank-to-offer-relief-for-coconut-cultivation-and-industry/31-645166#:~:text=Under%20the%20loan%2C%20customers%20can,repayment%20period%20is%20three%20years))) | 98 | | *Multiple banks*
(Participating banks via Central Bank refinance under IFAD-funded program) | **SAPP – Smallholder Agribusiness Partnership Programme** | **Smallholder farmers, farmer groups, and agri-entrepreneurs** in 4P partnerships (Public-Private-Producer Partnerships) for any **agribusiness** activity (including coconut cultivation or processing projects) ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=Areas%20for%20which%20credit%20is,provided)). Includes special categories for rural youth (ages 18–40) and private sector “promoter” companies collaborating with smallholders ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=,with%20SAPP%20endorsed%20by%20NSC)). | Varies by sub-loan category (e.g., up to ~Rs. 0.3 million for individual smallholders; up to Rs. 2.0 million for youth entrepreneurs; up to Rs. 18.0 million for private sector partner projects) ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=Maximum%20Amount)) | Up to 5 years (with 12–18 month grace period) ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=Maximum%20Amount)) | ~6.5% per annum (concessionary ([Best Business Development Loans in Sri Lanka | Low-Interest - People's Bank](https://www.peoplesbank.lk/development-loans/#:~:text=Interest%20Rate)); lower rates ~3.75% for certain financing intermediaries in the program) | 99 | 100 | **Information is based on publicly available sources as of April 2025. Loan terms may vary by bank or change over time. Applicants should confirm details with the respective financial institution or program authority before applying.* ([‘Kapruka Ayojana’ Credit Scheme (KACS) - DFCC Bank PLC](https://www.dfcc.lk/products/kapruka-ayojana-credit-scheme-kacs/#:~:text=,5%20Mn)) 101 | 102 | 103 | 104 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2025 Damith Rushika Kothalawala 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /OtherSuccessStories.md: -------------------------------------------------------------------------------- 1 | # Smart Agri-Tech Success Stories and Platforms in Asia 2 | 3 | ## Notable Success Stories (South Asia & ASEAN, 2015–2025) 4 | 5 | Below is a list of successful smart agri-tech initiatives (projects, pilots, startups, community efforts) in South Asia and ASEAN. These leverage AI, IoT, mobile platforms, or integrated systems to benefit tropical agriculture (including rice and coconut contexts where noted): 6 | 7 | | **Country** | **Project/Startup Name** | **Description** | **Link** | 8 | |-----------------|----------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------| 9 | | **Thailand** | **HandySense B-Farm** | A national smart farming platform launched in 2025 by Thailand’s NECTEC, using IoT sensors and AI to help farmers monitor and manage crops efficiently. Focuses on reducing costs and improving yield via real-time data and decision support. | [Nation Thailand](https://www.nationthailand.com/news/general/40046572) | 10 | | **Vietnam** | **MimosaTEK** | IoT-based farm management startup deploying field sensors and a cloud platform to optimize irrigation, fertilizer, and yields. Transforms traditional farming into data-driven practices. Recognized as one of Vietnam’s top agri-tech startups. | [Vietnam Briefing](https://www.vietnam-briefing.com/news/why-agtech-industry-will-aid-vietnams-hi-tech-growth.html) | 11 | | **Sri Lanka** | **Govi Mithuru** (“Farmers’ Friend”) | A mobile advisory service by Dialog Axiata and the Dept. of Agriculture (launched in 2015), offering voice/app-based farming advice for smallholders. Reached 600,000+ users by 2020. Supports food security and home gardening. | [Daily FT](https://www.ft.lk/Agriculture/Govi-Mithuru-by-Dialog-Axiata-empowers-Lankans-to-grow-their-own-food-in-home-gardens/31-705344) | 12 | | **Nepal** | **Smart Krishi** | An all-in-one mobile app (launched in 2015) for Nepali farmers. Offers crop/livestock guides, pest databases, and market info. Engages farmers via social platforms; video content reaches ~3 million monthly viewers. | [Kathmandu Post](https://kathmandupost.com/money/2023/08/05/smart-krishi-an-all-in-one-app-for-farmers) | 13 | | **Pakistan** | **Khushaal Zamindar** | A mobile IVR/SMS service by Telenor Pakistan (since 2015) delivering weather and agronomy advice to smallholders. By 2017, over 4 million users subscribed, 10% of them women. Enhances yield and climate resilience. | [Business Recorder](https://fp.brecorder.com/2017/12/20171206324676) | 14 | | **India** | **Cropin** | SaaS-based platform offering AI-powered farm management, crop monitoring, and predictive analytics. Digitized ~30 million acres, 7+ million farmers served globally. Useful in rice and coconut sectors. | [Cropin](https://cropin.com/about.html) | 15 | | **India** | **Fasal** | Precision agriculture startup using on-farm IoT sensors and AI for horticulture crops. Offers irrigation, pest/disease alerts, and nutrition guidance. Saved ~80B liters of water and covered 75,000+ acres. | [Mongabay India](https://india.mongabay.com/2024/04/farming-with-ai-and-drones-to-increase-yields-manage-resources-and-reduce-pests) | 16 | | **Indonesia** | **eFishery** | Aquaculture tech startup (founded 2013) offering IoT-powered fish feeders, financing, and market access. Supports 70,000+ fish farmers in 280+ cities. Raised yields by ~20–35%. | [SEADS (ADB)](https://seads.adb.org/articles/digital-aquaculture-business-solves-hyperlocal-problems-and-boosts-income-fish-and-shrimp) | 17 | | **Indonesia** | **JALA Tech** | Smart shrimp farming solution using IoT for water quality and a data platform for real-time farm decisions. Reported yield increases of up to 26%. Serves over 9,000 shrimp farmers. | [Unreasonable Group](https://unreasonablegroup.com/ventures/jala) | 18 | | **Bangladesh** | **iFarmer** | Fintech-driven agri platform (launched 2018) connecting farmers with financing, quality inputs, and direct markets. Active in 19 districts and supports 63,000+ farmers. | [AgFunderNews](https://agfundernews.com/ifarmer-banks-2-1m-pre-series-a-to-strengthen-ag-supply-chains-in-bangladesh) | 19 | 20 | **Sources:** Project websites and reputable news media (see inline links). 21 | -------------------------------------------------------------------------------- /PULL_REQUEST_TEMPLATE.md: -------------------------------------------------------------------------------- 1 | # Pull Request Template 2 | 3 | ## 📋 Summary 4 | 5 | Please provide a short summary of the changes in this pull request. Mention if the change is a bug fix, new feature, documentation update, or refactor. 6 | 7 | _Example: Added Sinhala translation to README, or Fixed API endpoint security issue_ 8 | 9 | --- 10 | 11 | ## ✅ Related Issue(s) 12 | 13 | Please link any related issues or discussions using GitHub references. 14 | 15 | _Example: Closes #14 or Related to #21_ 16 | 17 | --- 18 | 19 | ## 🔍 What Was Changed? 20 | 21 | Please describe in detail what was added, modified, or removed. Include any important technical or domain-specific context to help reviewers understand your changes. 22 | 23 | - 24 | - 25 | - 26 | 27 | --- 28 | 29 | ## 🌐 Languages / Areas Affected 30 | 31 | Tick all that apply: 32 | 33 | - [ ] Documentation 34 | - [ ] Sinhala / Tamil Localisation 35 | - [ ] Data Model / Schema 36 | - [ ] IoT Firmware / Hardware 37 | - [ ] Backend / API 38 | - [ ] Frontend / Dashboard 39 | - [ ] DevOps / Infrastructure 40 | - [ ] Other (please specify): ____________ 41 | 42 | --- 43 | 44 | ## 🧪 Testing Performed 45 | 46 | Please explain how the changes were tested. Include screenshots, logs, or test cases if possible. 47 | 48 | --- 49 | 50 | ## 📄 Checklist 51 | 52 | Please confirm the following before submitting your pull request: 53 | 54 | - [ ] I have read and followed the [CONTRIBUTING.md](./CONTRIBUTING.md) 55 | - [ ] My code/documentation is clear and well-commented where needed 56 | - [ ] I have included relevant unit or integration tests (if applicable) 57 | - [ ] I have reviewed all changes for security and ethical implications 58 | - [ ] I have verified that all text contributions follow English (UK) style 59 | - [ ] I have added proper attribution where third-party sources were used 60 | 61 | --- 62 | 63 | ## 🙏 Additional Notes 64 | 65 | If you have any special notes or points for discussion, please add them here. 66 | 67 | --- 68 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # CocoFusion 2 | 3 | *Combining AI and IoT to Revolutionize Coconut Farming in Sri Lanka* 4 | 5 | ## Overview 6 | 7 | CocoFusion is a **platform-agnostic, open-source research and development project** that combines **Artificial Intelligence (AI)** and the **Internet of Things (IoT)** to improve coconut farming practices in Sri Lanka. The goal is to fuse cutting-edge technology with traditional agriculture to help coconut farmers increase yields, use resources more efficiently, and make data-driven decisions in the field. By deploying smart sensors and AI models on coconut plantations, CocoFusion aims to provide farmers with real-time insights – from optimal irrigation scheduling to early pest detection – in a user-friendly way. Ultimately, the project seeks to boost sustainable coconut production in Sri Lanka while **bridging the gap between IT and agriculture** through collaborative innovation. 8 | 9 | ## Current Project Team as of 20th April 2025 10 | 11 | - Research Team 12 | - @damithkothalawala - Cloud / IoT 13 | - Repo Management Team 14 | - @madurapa 15 | - PM Team 16 | - @lochana-d 17 | 18 | ## We Are Looking For 19 | 20 | - AgriTech Doctors/Professor(s) – Research 21 | - IT Doctors/Professor(s) – Research 22 | - Volunteer Project Managers with GitHub Projects Experience 23 | - DPI/DPG Advisor to validate and set guidelines to align with Sri Lanka Standards 24 | - Agriculture Extension Officers with Coconut Field Experience 25 | - Soil Scientists specialised in tropical/humid climate conditions 26 | - IoT / Embedded Systems Engineers familiar with precision agriculture 27 | - Cloud Engineers / DevOps Experts for backend infrastructure 28 | - Data Scientists or Analysts with interest in agri-yield optimisation 29 | - Technical Writers to support multi-language documentation (Sinhala, Tamil, English) 30 | - Translators / Language Volunteers for accurate localisation 31 | - Agribusiness Consultants with knowledge of market linkages 32 | - University Students (Agriculture / IT) willing to contribute towards research and field validation 33 | - Open Source Contributors familiar with GitHub collaboration practices 34 | - Community Moderators to manage issue threads and discussions 35 | - Legal Advisors familiar with data licensing, open-source policies, and Sri Lankan research ethics 36 | 37 | Please join the [introduction thread](https://github.com/damithkothalawala/CocoFusion/discussions/28) to introduce yourself and share your work or interests. 38 | 39 | ## 🧑‍🌾 CocoFusion Project – Participant Directory (will be moved to a different location as a file later) 40 | 41 | This section contains contributors who expressed interest in the CocoFusion initiative, their affiliations, and key expertise areas. 42 | 43 | | GitHub Username | Affiliation | Area of Interest | 44 | |-----------------|-------------|------------------| 45 | | @isum03 | University of Westminster | | 46 | | @roshanRishantha2004 | Esoft Collage of Engineering| and Technology (Pearson) | 47 | | @sozibalmamun | THT Space Electrical Company Ltd. | IoT and embedded Systems | 48 | | @HirushaNaveen | University of Kelaniya | IoT and embedded Systems | 49 | | @DamithraFdo | Rajarata University of Sri Lanka | | 50 | | @AnsarMahir | UoM - IT fac | | 51 | | @ahmedstki | Rajarata University / BCS | AgriTech | 52 | | @Rathnamalala | General sir john kotelawala defence university - KDU. | AI/ML SE | 53 | | @pradeeep1999 | | IoT and embedded Systems | 54 | | @ilsam99 | SLIIT | IoT and embedded Systems / Cloud | 55 | | @Sumindu | Esoft Metro Campus | AI/ML SE | 56 | | @thilinapremachandra | Rajarata University | AI/ML SE / IoT | 57 | | @JithmiKumarasingha | University of Moratuwa | Embedded Systems, C, Java, Python | 58 | | @kailash3590 | Agripreneur | Agro Techniques, Irrigation Systems | 59 | 60 | ## Aligning with Sri Lanka's National DPI/DPG Standards 61 | Sources Credits Goes to: @dasunhegoda 62 | 63 | Our goal is to align and support national DPI/DPG Initiative while not doing another duplicate research. 64 | 65 | Future State of Sri Lanka DPI/DPG 66 | 67 | So the contributors should carefully refer following documents hosted at Ministry of Agriculture in Sri Lanka 68 | 69 | | Document Name | Link | 70 | |:--------------------|:------------------------------------------| 71 | |Inclusive Digital Agriculture Transformation (IDAT) in Sri Lanka | Download| 72 | |AGRICULTURE ENTERPRISE ARCHITECTURE | Download| 73 | |AGRICULTURE INTEROPERABILITY FRAMEWORK FOR AGRICULTURE | Download| 74 | 75 | ## Background: Why Coconut Farming in Sri Lanka? 76 | 77 | ![CocoFusion Project](https://www.stvincenttimes.com/wp-content/uploads/2025/01/MOA-23.webp) 78 | 79 | Coconut farming is central to Sri Lanka’s economy and culture. The country produces about **2.8–3.0 billion nuts per year**, yet demand is around **4 billion** nuts – indicating a significant shortfall ([Coconut and Coconut Based Products - Industry Capability Report](https://www.srilankabusiness.com/coconut/about/industry-capability.html#:~:text=existing%20demand%20for%20coconuts%20in,0%20billion)) ([Coconut and Coconut Based Products - Industry Capability Report](https://www.srilankabusiness.com/coconut/about/industry-capability.html#:~:text=The%20annual%20coconut%20production%20in,target%20of%204%2C000%20million%20nuts)). Coconut products make up roughly **12% of Sri Lanka’s agricultural output**, supporting the livelihoods of approximately **700,000 people** ([Coconut and Coconut Based Products - Industry Capability Report](https://www.srilankabusiness.com/coconut/about/industry-capability.html#:~:text=existing%20demand%20for%20coconuts%20in,0%20billion)) ([Coconut and Coconut Based Products - Industry Capability Report](https://www.srilankabusiness.com/coconut/about/industry-capability.html#:~:text=According%20to%20the%20Coconut%20Research,indirect%20employment%20to%20another%20135%2C000)). Most coconuts are grown by smallholder farmers (about **75%** of plantations) who contribute ~70% of production, but many of these farms are managed below optimal levels ([Coconut and Coconut Based Products - Industry Capability Report](https://www.srilankabusiness.com/coconut/about/industry-capability.html#:~:text=The%20major%20portion%20of%20coconut,way%20below%20the%20optimal%20levels)). This gap between current yields and potential capacity highlights the need for innovative solutions. 80 | 81 | Recent studies have shown that introducing IoT and smart farming techniques can significantly benefit coconut cultivation. For example, using IoT-based irrigation control has improved ROI and yield efficiency for coconut farms while reducing water and energy usage ([The Investigation of Benefits and Challenges of Using IoT Technology to Enhance the Irrigation Method of Coconut Farming in Sri Lanka by Sathiyamoorthy M, Sarathchandra A.W.C.K. :: SSRN](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4115643#:~:text=findings%20proved%20that%20not%20only,every%20coconut%20farm%20holder%20in)). At the same time, **AI-powered sensors** can monitor soil conditions, humidity, and tree health in real-time – helping farmers optimize irrigation and detect pest infestations early ([Leveraging AI and GPT to Drive Sustainable Innovation in Coconut-based Agriculture Manufacturing](https://www.linkedin.com/pulse/leveraging-ai-gpt-drive-sustainable-innovation-kelum-wickramasinghe#:~:text=smarter%20cultivation%20practices.%20AI,and%20increasing%20overall%20yield%20quality)). These insights reduce the reliance on guesswork and can lead to **higher yields and more sustainable practices**. However, challenges such as limited technology awareness and poor internet connectivity in rural areas remain ([The Investigation of Benefits and Challenges of Using IoT Technology to Enhance the Irrigation Method of Coconut Farming in Sri Lanka by Sathiyamoorthy M, Sarathchandra A.W.C.K. :: SSRN](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4115643#:~:text=findings%20proved%20that%20not%20only,every%20coconut%20farm%20holder%20in)). CocoFusion is designed to address these challenges by developing an open, adaptable system that is easy for local farmers and stakeholders to use. By focusing on Sri Lanka’s coconut sector, where the need and impact are high, we hope to demonstrate how technology can uplift traditional farming communities. 82 | 83 | ## Project Goals 84 | 85 | CocoFusion’s goals are centered on empowering coconut farmers and modernizing agricultural practices. Key objectives include: 86 | 87 | - **Increase Coconut Yield and Quality:** Use data-driven insights (e.g. predictive analytics for crop health and growth) to help farmers produce more nuts and improve crop quality, narrowing the gap between current production and the 4 billion nuts/year demand ([Coconut and Coconut Based Products - Industry Capability Report](https://www.srilankabusiness.com/coconut/about/industry-capability.html#:~:text=existing%20demand%20for%20coconuts%20in,0%20billion)) ([Coconut and Coconut Based Products - Industry Capability Report](https://www.srilankabusiness.com/coconut/about/industry-capability.html#:~:text=The%20annual%20coconut%20production%20in,target%20of%204%2C000%20million%20nuts)). 88 | - **Optimize Resource Usage:** Implement smart irrigation and fertilization schedules based on real-time field data, ensuring efficient use of water, soil nutrients, and electricity (preventing waste and reducing costs) ([The Investigation of Benefits and Challenges of Using IoT Technology to Enhance the Irrigation Method of Coconut Farming in Sri Lanka by Sathiyamoorthy M, Sarathchandra A.W.C.K. :: SSRN](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4115643#:~:text=findings%20proved%20that%20not%20only,every%20coconut%20farm%20holder%20in)). 89 | - **Early Pest and Disease Detection:** Deploy sensors and AI models to monitor for signs of pest infestations or diseases (such as coconut rhinoceros beetle or leaf rot) so that farmers can intervene early ([Leveraging AI and GPT to Drive Sustainable Innovation in Coconut-based Agriculture Manufacturing](https://www.linkedin.com/pulse/leveraging-ai-gpt-drive-sustainable-innovation-kelum-wickramasinghe#:~:text=smarter%20cultivation%20practices.%20AI,and%20increasing%20overall%20yield%20quality)). This helps protect coconut palms and reduces heavy chemical use by targeting issues precisely. 90 | - **Support Smallholder Farmers:** Develop solutions that are affordable and **platform-agnostic** – able to run on various hardware (from Arduino/Raspberry Pi to commercial sensor kits) and on local or cloud servers. This flexibility means even farmers with limited resources or connectivity can benefit. 91 | - **Interdisciplinary Collaboration:** Promote active collaboration between **Information Technology and Agriculture** communities. CocoFusion brings together software developers, data scientists, agronomists, and coconut farmers to co-create technology that is grounded in real agricultural needs. Through this, IT students and faculty work alongside agricultural students and experts, fostering knowledge exchange and innovative thinking. 92 | - **Open Knowledge and Sustainability:** Make all findings, data, and tools openly accessible. The project will document best practices in smart coconut farming, creating a knowledge base that educators and policymakers can use to drive wider adoption of precision agriculture in Sri Lanka and beyond. 93 | 94 | ## Project Plan (Phases) 95 | 96 | CocoFusion will be executed in well-defined phases to ensure a structured approach. Each phase involves both tech and agriculture stakeholders working together: 97 | 98 | 1. **Phase 1: Site Assessment & Planning** – We begin by understanding the coconut farm environments where CocoFusion will be piloted. This phase involves **field visits to coconut plantations** to identify local needs and constraints. The team (including agriculture experts and students) will survey the land and current farming practices: soil types, climate patterns, common pests/diseases, and farmer pain points. We will also assess practical factors like internet connectivity, power availability, and the farmers’ familiarity with technology. By the end of this phase, we’ll have a clear requirements outline and a tailored plan for each pilot site (e.g. which metrics to monitor, where to place sensors, and what AI insights would be most valuable). Building relationships with the farmers and getting their input is a key part of this phase to ensure the technology addresses real problems. 99 | 100 | 2. **Phase 2: IoT Setup (Hardware Deployment)** – Based on the site assessment, we will deploy a network of IoT sensors and devices on the farm. This includes selecting appropriate **sensors for environmental and crop data** – for example, soil moisture probes, temperature and humidity sensors, rainfall gauges, light sensors, and possibly **camera traps or drone imaging** for monitoring tree health. Each sensor node will be strategically placed to get good coverage of the farm ([Paper Title (use style: paper title)](https://www.afjbs.com/uploads/paper/e9ed1bf80a2a90f1cb5cb671248d488a.pdf#:~:text=implementation,data%20collection%20and%20field%20coverage)). We’ll use robust, low-cost hardware (e.g. Arduino or Raspberry Pi with wireless modules) and ensure connectivity (such as setting up a long-range LoRaWAN network or using 4G where available) so that data can transmit to a central system. The IoT setup will be **platform-agnostic and modular**, meaning farmers can use different brands or types of sensors as available. During this phase, we also create the **data pipeline** – e.g. a gateway device that collects sensor readings and sends them to a cloud database or local server. Importantly, we will train the local farmers or field staff on basic device maintenance (like charging a solar battery or resetting a device) so they feel comfortable with the equipment. By the end of Phase 2, the farm will be “online,” continuously collecting real-time data. 101 | 102 | 3. **Phase 3: Data Collection & Integration** – Once the sensors are in place, CocoFusion will collect data over a period of time (weeks to months) to build a rich dataset for analysis. All sensor readings (soil moisture, temperature, humidity, etc.) stream into a central database where they are time-stamped and stored. We will also integrate **external data sources** that impact coconut farming – for instance, weather forecasts, satellite imagery, or drone aerial photos of the coconut canopy. Additionally, farmers and agronomists will help collect **ground truth data**: examples include recording the actual yield from each harvest, noting occurrences of pests or disease symptoms, and logging farm management activities (watering, fertilizing, etc.). This combined dataset will give a 360° view of the farm’s conditions and outcomes. Throughout this phase, we verify data quality and make adjustments – if a sensor is not reliable or placed incorrectly, we fix it. By Phase 3’s conclusion, we’ll have a well-organized dataset that links environmental conditions to coconut plant performance. This data is the foundation for building AI models in the next phase ([Enhanced Coconut Yield Prediction Using Internet of Things and Deep Learning: A Bi-Directional Long Short-Term Memory Lévy Flight and Seagull Optimization Algorithm Approach](https://www.mdpi.com/2076-3417/14/17/7516#:~:text=Stage%201%3A%20Initially%2C%20the%20agricultural,rainfall%2C%20precipitation%2C%20and%20solar%20radiation)) ([Paper Title (use style: paper title)](https://www.afjbs.com/uploads/paper/e9ed1bf80a2a90f1cb5cb671248d488a.pdf#:~:text=Data%20collection%20starts%20as%20soon,other%20environmental%20conditions%20affecting%20agricultural)). 103 | 104 | 4. **Phase 4: AI Modeling & Analysis** – With real farm data in hand, the project’s focus shifts to analysis and developing intelligent models. Our data science team (which can include university students and researchers) will explore the data to find patterns and insights. We’ll use a combination of **machine learning (ML)** and simple heuristic approaches to address key questions: *Can we predict the optimal irrigation schedule to maximize yield? Can we detect early warning signs of pest infestation or nutrient deficiency from sensor trends or images?* In practice, this might involve training models – for example, a predictive model using environmental sensor data to forecast soil moisture levels and recommend when to irrigate, or a computer vision model to analyze drone images for spotting discoloration on coconut palms that indicates disease. We plan to start with interpretable models (like threshold alerts or regression models) for quick wins, and gradually introduce more advanced techniques (like neural networks for pattern recognition) as data grows. The AI models will be evaluated rigorously: we’ll split data into training and test sets, measure accuracy of predictions (e.g. did the yield prediction match actual yield?), and refine accordingly. Crucially, agronomists in the team will validate whether the model outputs make practical sense. The outcome of Phase 4 will be a set of **prototype AI tools** – for instance, an alert system that texts the farmer “Soil moisture low in north field, consider watering tomorrow,” or a dashboard showing a health score for each coconut tree. These tools harness the data to provide actionable recommendations ([Paper Title (use style: paper title)](https://www.afjbs.com/uploads/paper/e9ed1bf80a2a90f1cb5cb671248d488a.pdf#:~:text=including%20machine%20learning%20and%20deep,farmers%20useful%20knowledge%20to%20improve)). All algorithms and code will be open-source in this repository, inviting peer review and improvements. 105 | 106 | 5. **Phase 5: Field Trials & Feedback** – Having developed initial IoT and AI solutions, we will test them in the real world through field trials. In this phase, the CocoFusion system (sensors + AI software) will be actively used by volunteer pilot farms or experimental plots. For example, one trial might involve a **smart irrigation experiment**: one section of the coconut farm uses CocoFusion’s AI-driven irrigation alerts, while another section is managed normally, and we compare the soil moisture and yield outcomes. Another trial could test the **pest detection system** by seeing if the AI alerts catch an infestation earlier than farmers normally would. Throughout the trials, we will gather feedback from the farmers and field personnel using the system. We want to know: *Are the recommendations clear and useful? Is the mobile app or interface easy to understand? Does the technology fit into daily farming routines without hassle?* Any issues (false alarms, sensor failures, etc.) will be documented. Agronomy students and faculty may conduct surveys or interviews with the farmers to formally evaluate the system’s impact on decision-making and farm outcomes. At the same time, we’ll measure objective results – such as changes in yield, reduction in water used, or quicker pest control responses – to quantify benefits. This phase is iterative: if problems are found, we go back and improve the hardware or tweak the AI model, then trial again. By the end of Phase 5, we aim to have proven the concept: concrete evidence of CocoFusion’s effectiveness (like a percentage increase in yield or resource savings) and a refined system that incorporates user feedback. 107 | 108 | 6. **Phase 6: Refinement, Expansion & Knowledge Sharing** – After successful field trials, CocoFusion will refine the prototypes into a more robust **deployable solution**. Any remaining bugs will be fixed, and the system will be made more user-friendly based on farmer feedback (for instance, local language support in the interface or adding any features farmers suggested). We will update documentation so that others can set up the system more easily. At this stage, the project transitions from pilot to broader deployment: we plan to **expand to more farms** in different regions of Sri Lanka to test CocoFusion in varied conditions (different climate zones or soil types) and ensure it generalizes well. Meanwhile, the team will actively share knowledge gained. This includes publishing results and guides in this repository (and possibly research papers or blog posts), conducting workshops or seminars for local farming communities, and reaching out to Sri Lanka’s Coconut Research Institute or agriculture ministries to disseminate findings. We will also encourage other universities (IT and agriculture faculties) to adopt CocoFusion as a student project, thereby **scaling the collaboration**. The ultimate goal is for CocoFusion to become a self-sustaining open-source initiative that communities can adopt and adapt. By Phase 6’s end, we hope to see a growing community of users and contributors, beyond the original pilot, using CocoFusion to make coconut farming smarter and more sustainable. 109 | 110 | *(Note: The above phases are a roadmap and may evolve. We anticipate overlapping some phases in practice – for instance, continuing data collection and minor field testing while modeling is ongoing. The project is iterative and will incorporate lessons learned at each step.)* 111 | 112 | ## Contributing 113 | 114 | We warmly welcome contributions from **anyone** who is interested in CocoFusion’s mission! This project thrives on interdisciplinary collaboration, bringing together people with different skills – you do **not** have to be a software developer to contribute. In fact, one of CocoFusion’s core values is to unite tech enthusiasts with agricultural practitioners. Whether you’re a seasoned coder, a student, or a coconut farmer, there are many ways you can get involved: 115 | 116 | - **🌐 Software Developers & IT Students:** Help build and improve the CocoFusion platform. This can include coding the firmware for sensor devices, developing the backend data pipeline, or creating user-friendly mobile/web applications for farmers. Even if you’re new to coding, you can assist in writing scripts to analyze data or visualize sensor readings. We use open-source tools and will gladly mentor newcomers. Check the issue tracker for software feature requests or bugs to fix, or propose your own enhancements! 117 | - **🤖 AI/ML Engineers & Data Scientists:** If you have skills in data analysis or machine learning, join us in crafting the AI models that drive CocoFusion’s insights. You can contribute by cleaning and labeling data, experimenting with algorithms for predictions (e.g. rainfall forecasting, yield prediction), or improving the accuracy of our pest detection models. We encourage sharing new model ideas – for example, maybe you want to try a computer vision approach to count coconuts from tree images, or use deep learning to optimize irrigation scheduling. Your expertise will help turn raw farm data into meaningful guidance for farmers. 118 | - **📡 IoT Enthusiasts & Hardware Experts:** CocoFusion involves setting up hardware in the field, so we need people who love tinkering with gadgets. You can contribute by designing and testing sensor circuits, improving the durability of devices (weather-proof enclosures, solar power setups), or integrating new types of sensors (e.g. a coconut tree trunk borer detector). If you’re familiar with technologies like Arduino, Raspberry Pi, or networking protocols (LoRaWAN, MQTT), your knowledge is invaluable. Non-coders can help assemble devices or deploy them on-site. Documenting hardware assembly steps is another great way to contribute. 119 | - **🌱 Agronomists & Agriculture Students:** We heavily rely on domain experts to ensure the project’s success. If you have a background in agriculture or plant science, you can guide what data is important to collect and help interpret the results. For instance, you might advise on the signs of nutrient deficiency in coconut palms or validate that our pest alerts make sense biologically. You can also conduct field experiments in collaboration with the tech team and suggest improvements from an agricultural perspective. Your contributions ensure CocoFusion’s solutions are scientifically sound and truly practical for farmers. 120 | - **🚜 Coconut Farmers & Field Data Collectors:** Farmers are the heartbeat of CocoFusion. If you are a coconut grower or work with farmers, your participation is extremely valuable. You can contribute by **hosting pilot tests** on your farm, providing feedback on the technology (what works, what doesn’t), and suggesting features that would make your life easier. Even simply sharing your day-to-day farming challenges can help the developers tailor the solution better. Additionally, you can assist in data collection by keeping logs of farm activities or helping to ground-truth sensor data (for example, confirming that a high soil moisture reading corresponds to recent rain). No technical experience is needed – just your willingness to try new tools and tell us honestly how they perform. 121 | - **🛰️ Drone Operators & Remote Sensing Specialists:** If you have access to a drone or expertise in remote sensing, you can contribute by capturing aerial images or videos of coconut plantations. These images can be used to assess tree health, count coconut yields, or detect patterns (like irrigation coverage or pest damage) that ground sensors might miss. You could help build maps of the pilot sites or even contribute to developing drone-based surveys. This is a great way for tech hobbyists (e.g. drone enthusiasts at universities or local clubs) to support the project. 122 | - **✍️ Documentation Writers & Educators:** You don’t need to be on a farm or writing code to help CocoFusion. We need people to improve our documentation, create tutorials, and translate materials for farmers. If you enjoy writing or teaching, you can ensure that our README, setup guides, and user manuals are clear for everyone – including non-technical users. Creating infographics or video demos explaining how CocoFusion works in simple terms is another wonderful contribution. By helping with documentation and outreach, you make the project more accessible and friendly to newcomers. 123 | 124 | **How to Get Started:** If you’d like to contribute, please check out our [Contributing Guidelines](CONTRIBUTING.md) (if available) or simply reach out by opening an issue/discussion on this repository. You can also contact the project maintainers directly if you have questions or want to discuss an idea. New contributors are always welcome – don’t hesitate to introduce yourself and share how you’d like to help. We strive to maintain a positive and inclusive atmosphere. Mentorship can be provided for students or anyone new to open source. Every type of contribution, no matter how small, is appreciated. 💚 125 | 126 | By contributing to CocoFusion, you’re not just writing code or collecting data – you’re becoming part of a community that bridges tech and agriculture for social good. We believe that **together**, we can co-create solutions that make a real difference for coconut farmers and set an example of interdisciplinary innovation. 127 | 128 | ## License 129 | 130 | This project is licensed under the **MIT License**. Feel free to use, modify, and distribute the code and materials in accordance with the license. (See the [LICENSE](LICENSE) file for details.) 131 | 132 | ## Disclaimer 133 | 134 | **Disclaimer:** The initial project plan for CocoFusion (including much of the content in this README) was generated by ChatGPT based on input from **Damith Rushika Kothalawala**, who is a cloud technology expert *not* an agricultural expert. This plan is a starting point and may contain assumptions that need validation in the real world. As the project progresses, we expect and **welcome contributions from coconut farming specialists, agronomists, and domain experts** to refine and improve the plan. CocoFusion is a community-driven effort – the ideas here will evolve with feedback from those with on-the-ground agricultural experience, ensuring that the project remains realistic and beneficial to its intended users. Thank you for your understanding and for helping us improve CocoFusion with your expertise! 135 | 136 | -------------------------------------------------------------------------------- /ReadMore.md: -------------------------------------------------------------------------------- 1 | # Smart Agriculture Innovations in Coconut and Tropical Crops (2015–2025) 2 | 3 | Below is a curated list of freely available research papers, government/NGO reports, and case studies from 2015–2025 focusing on **smart agriculture, AI/IoT applications, and digital innovations** in coconut and other tropical crop farming. The entries are grouped by country (India, Sri Lanka, Malaysia, Indonesia), as these regions share relevance to coconut farming in Sri Lanka. Each entry includes the title, authors, year, a brief summary, and a link to access the publication. 4 | 5 | ## Sri Lanka 6 | 7 | - **The Investigation of Benefits and Challenges of Using IoT Technology to Enhance the Irrigation Method of Coconut Farming in Sri Lanka** (2021) – *M. Sathiyamoorthy, A.W.C.K. Sarathchandra*. 8 | *Summary:* This paper examines the adoption of **IoT-based smart irrigation** in Sri Lankan coconut plantations. A qualitative study (interviews with a farm using an IoT irrigation solution and the solution provider) found that IoT adoption in coconut irrigation yielded significant benefits: higher ROI and efficiency, increased yields, and reduced water and electricity use ([The Investigation of Benefits and Challenges of Using IoT Technology to Enhance the Irrigation Method of Coconut Farming in Sri Lanka by Sathiyamoorthy M, Sarathchandra A.W.C.K. :: SSRN](https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID4178780_code4386120.pdf?abstractid=4115643#:~:text=Internet%20of%20Things%20,efficiency%2C%20and%20decreased%20water%20and)). It also identifies challenges such as limited farmer tech knowledge and poor internet access, suggesting the need for training and support to realize IoT’s full value ([The Investigation of Benefits and Challenges of Using IoT Technology to Enhance the Irrigation Method of Coconut Farming in Sri Lanka by Sathiyamoorthy M, Sarathchandra A.W.C.K. :: SSRN](https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID4178780_code4386120.pdf?abstractid=4115643#:~:text=electricity%20consumption,to%20enhance%20the%20coconut%20sector)). The study concludes that IoT-driven irrigation provides a competitive advantage and recommends scaling IoT adoption across Sri Lanka’s coconut farms for greater productivity. 9 | *Link:* [SSRN paper](https://ssrn.com/abstract=4115643) (open access) 10 | 11 | - **Coconut Disease Prediction System Using Image Processing and Deep Learning Techniques** (2020) – *D. Nesarajan, L. Kunalan, M. Logeswaran, S. Kasthuriarachchi, D. Lungalage*. 12 | *Summary:* Researchers at SLIIT (Sri Lanka) developed an **AI-powered system** to detect coconut tree diseases and pest infestations at an early stage. The project used image processing and machine learning (CNN) on coconut leaf images to identify pest attacks and nutrient deficiencies, with the goal of alerting farmers early ([Coconut Disease Prediction System Using Image Processing and Deep Learning Techniques | CoLab](https://colab.ws/articles/10.1109%2FIPAS50080.2020.9334934#:~:text=Coconut%20production%20is%20the%20most,monitorization%20has%20been%20taken%20place)). The solution includes an **Android mobile application** that can identify specific pests by their feeding patterns and diagnose diseases or nutrient issues from leaf photos ([Coconut Disease Prediction System Using Image Processing and Deep Learning Techniques | CoLab](https://colab.ws/articles/10.1109%2FIPAS50080.2020.9334934#:~:text=in%20the%20coconut%20leaves%20very,processing%20steps%20such)). Early detection and analysis enable timely intervention, helping improve coconut yield and farmer livelihoods. 13 | *Link:* *Conference paper (IEEE IPAS 2020)* – *Full text available via ResearchGate* 14 | 15 | - **Sri Lanka National E-Agriculture Strategy** (2016) – *Ministry of Agriculture, Sri Lanka; supported by FAO and ITU*. 16 | *Summary:* This government strategy document outlines a **roadmap for integrating ICT and digital solutions in agriculture**. It provides an analysis of current and future ICT roles in Sri Lankan agriculture and recommends an action plan to mainstream e-agriculture across sectors ([Sri Lanka E-agriculture Strategy](https://faolex.fao.org/docs/pdf/srl169703.pdf#:~:text=Executive%20Summary%20The%20Sri%20Lanka,up%20and%20supported%20through%20the)). The strategy emphasizes a collaborative framework to scale up pilot ICT projects, and it leverages existing ICT developments (e.g. mobile apps, databases, decision support systems) to improve farming efficiency and knowledge sharing. It captures emerging technologies (e.g. remote sensing, precision farming tools) and recommends steps for their adoption to transform agriculture for national prosperity ([Sri Lanka E-agriculture Strategy](https://faolex.fao.org/docs/pdf/srl169703.pdf#:~:text=Innovations%20in%20ICT%20is%20happening,steering%20committee%20and%20task%20force)). Key recommendations include improving data access, developing affordable ICT platforms for farmers, enhancing digital literacy of stakeholders, and promoting innovation in e-agriculture services. 17 | *Link:* [Government Report PDF](http://www.fao.org/3/a-br419e.pdf) (FAO/ITU published strategy) 18 | 19 | ## India 20 | 21 | - **A Study on IoT-Based Low-Cost Smart Kit for Coconut Farm Management** (2020) – *S. Jaisankar, P. Nalini, K.K. Rubigha*. 22 | *Summary:* This conference paper (I-SMAC 2020) proposes a **low-cost “Coco Smart Kit”** to help small-scale Indian coconut farmers adopt IoT in farm management ([Iot Based On coconut farm management](https://www.linkedin.com/pulse/iot-based-coconut-farm-management-priya-dharshini-%E3%83%97%E3%83%AA%E3%83%BC%E3%83%A4%E3%83%80%E3%83%BC%E3%82%B7%E3%83%8B-#:~:text=Affordability%20of%20smart%20devices%20by,efficiently%20without%20any%20manpower%20resources)). High cost is a barrier to IoT uptake, so the authors designed an affordable kit with sensors that connect to a farmer’s smartphone ([Iot Based On coconut farm management](https://www.linkedin.com/pulse/iot-based-coconut-farm-management-priya-dharshini-%E3%83%97%E3%83%AA%E3%83%BC%E3%83%A4%E3%83%80%E3%83%BC%E3%82%B7%E3%83%8B-#:~:text=Affordability%20of%20smart%20devices%20by,efficiently%20without%20any%20manpower%20resources)). The system enables real-time monitoring of farm conditions (e.g. soil moisture, climate) and automates certain tasks, allowing farmers to manage irrigation and other operations more efficiently **with minimal labor** ([Iot Based On coconut farm management](https://www.linkedin.com/pulse/iot-based-coconut-farm-management-priya-dharshini-%E3%83%97%E3%83%AA%E3%83%BC%E3%83%A4%E3%83%80%E3%83%BC%E3%82%B7%E3%83%8B-#:~:text=that%20small%20farmers%20are%20facing,efficiently%20without%20any%20manpower%20resources)). By tackling affordability and ease-of-use, this IoT kit aims to make precision farming accessible to resource-limited coconut growers. 23 | *Link:* *IEEE Conference Paper* – *Available via IEEE Xplore (DOI: 10.1109/I-SMAC49090.2020.9243486)* 24 | 25 | - **Pest Infestation Identification in Coconut Trees Using Deep Learning** (2019) – *Abraham Chandy*. 26 | *Summary:* This study demonstrates a **drone-based AI system** for automatic pest detection in coconut plantations ([PEST INFESTATION IDENTIFICATION IN COCONUT TREES USING DEEP LEARNING | IRO Journals](https://irojournals.com/aicn/article/view/1/1/2#:~:text=In%20this%20paper%2C%20we%20propose,treatment%20of%20pest%20infected%20trees)). A camera-equipped drone is used to capture images of coconut palms, and a deep learning model (running on an NVIDIA Tegra SoC onboard) processes these images to detect pest-infested or diseased trees in real time ([PEST INFESTATION IDENTIFICATION IN COCONUT TREES USING DEEP LEARNING | IRO Journals](https://irojournals.com/aicn/article/view/1/1/2#:~:text=In%20this%20paper%2C%20we%20propose,treatment%20of%20pest%20infected%20trees)). When the AI identifies an unhealthy tree, it wirelessly sends an alert and the processed images to the farmer’s smartphone via Wi-Fi ([PEST INFESTATION IDENTIFICATION IN COCONUT TREES USING DEEP LEARNING | IRO Journals](https://irojournals.com/aicn/article/view/1/1/2#:~:text=with%20a%20camera%20interfaced%20drone,the%20yield%20of%20the%20trees)). This allows for timely intervention (e.g. targeted pesticide application), thereby preventing pest spread and improving overall yield. The approach exemplifies precision agriculture by combining UAVs and AI to monitor crop health automatically. 27 | *Link:* *Journal of Artificial Intelligence and Capsule Networks, Vol.1(1) 2019* (Open Access) – [Full PDF](https://irojournals.com/aicn/article/view/1/1/2) available 28 | 29 | - **Extent of Utilization of Android App on Coconut Expert System and Its Effectiveness as Perceived by Coconut Farmers in Tamil Nadu, India** (2019) – *A. Aravind, R. Raja Sekaran, C. Karthikeyan, R. Gangai Selvi*. 30 | *Summary:* This extension research (published in *Int. J. Curr. Microbiol. App. Sci.*) evaluates a **mobile-based Coconut Expert System app** introduced to coconut growers in Kanyakumari District, India. The study surveyed 120 farmers on how much they used the Android app and how effective they found it. Results showed that many farmers found the expert system’s information (on cultivation practices, pest/disease management, etc.) useful, but some desired more user-friendly features and more localized content ([ ](https://www.ijcmas.com/8-11-2019/A.%20Aravind,%20et%20al.pdf#:~:text=private%20institutions%20and%20adopted%20by,expert%20system%20can%20be%20assessed)) ([ ](https://www.ijcmas.com/8-11-2019/A.%20Aravind,%20et%20al.pdf#:~:text=of%20the%20expert%20system%20to,their%20skills%20and%20effective%20utilization)). The authors note that farmers’ tech-savviness varied – younger and educated farmers used the app more. The study recommends more training, awareness campaigns, and content customization to improve the app’s utilization and impact. These findings help developers and extension agencies refine digital advisory tools for better farmer adoption ([ ](https://www.ijcmas.com/8-11-2019/A.%20Aravind,%20et%20al.pdf#:~:text=private%20institutions%20and%20adopted%20by,expert%20system%20can%20be%20assessed)) ([ ](https://www.ijcmas.com/8-11-2019/A.%20Aravind,%20et%20al.pdf#:~:text=of%20the%20expert%20system%20to,their%20skills%20and%20effective%20utilization)). 31 | *Link:* [Journal Article PDF](https://www.ijcmas.com/8-11-2019/A.%20Aravind,%20et%20al.pdf) (open access) 32 | 33 | ## Malaysia 34 | 35 | - **Unmanned Aerial Vehicle (UAV)-Based Remote Sensing for Early-Stage Detection of *Ganoderma*** (2022) – *Parisa Ahmadi, Syed Abdullah M. Mansor, Behzad Farjad, Ehsan Ghaderpour*. 36 | *Summary:* This open-access study addresses the serious **Basal Stem Rot (Ganoderma) disease** in oil palms, a major plantation crop in Malaysia and Indonesia. The researchers evaluated using **drone-mounted cameras and AI (Artificial Neural Networks)** to detect Ganoderma infection in oil palm trees before visible symptoms appear. By analyzing multispectral imagery (including near-infrared) from a low-flying UAV, they identified subtle canopy changes associated with early-stage infection ([Unmanned Aerial Vehicle (UAV)-Based Remote Sensing for Early-Stage Detection of Ganoderma](https://www.mdpi.com/2072-4292/14/5/1239#:~:text=Early%20detection%20of%20Basal%20Stem,the%20use%20of%20remote%20sensing)) ([Unmanned Aerial Vehicle (UAV)-Based Remote Sensing for Early-Stage Detection of Ganoderma](https://www.mdpi.com/2072-4292/14/5/1239#:~:text=technique%20for%20the%20rapid%20detection,infrared%2C%201%2F8%20threshold%20limit%2C%20and)). An ANN model was trained on these image features to classify palms by infection level. The optimized model achieved about 72.7% accuracy in distinguishing early infected palms in testing ([Unmanned Aerial Vehicle (UAV)-Based Remote Sensing for Early-Stage Detection of Ganoderma](https://www.mdpi.com/2072-4292/14/5/1239#:~:text=different%20Ganoderma%20severity%20levels,UAV%20images%20integrated%20with%20the)). The study demonstrates that an affordable drone with a modified camera, coupled with AI analysis, can serve as a rapid surveillance tool for plantation disease management ([Unmanned Aerial Vehicle (UAV)-Based Remote Sensing for Early-Stage Detection of Ganoderma](https://www.mdpi.com/2072-4292/14/5/1239#:~:text=ANN%20network%20by%20219%20hidden,a%20rapid%20and%20inexpensive%20manner)). Early detection via UAV remote sensing allows planters to remove or treat infected palms sooner, reducing spread and economic losses. 37 | *Link:* [Remote Sensing Journal (MDPI) – Full Article](https://doi.org/10.3390/rs14051239) (open access) 38 | 39 | - **Driving Agricultural Transformation: Unraveling Key Factors Shaping IoT Adoption in Smart Farming** (2023) – *Mahadi Bahari et al.* 40 | *Summary:* This is a recent Malaysian study (Sustainability, 2023) investigating the **drivers and barriers of IoT adoption among farmers**. Through a survey of 179 agricultural managers in Malaysia, the authors apply a Technology-Organization-Environment framework to identify factors influencing uptake of IoT-based farming. The findings highlight that **government support** and technological compatibility are crucial positive factors for IoT adoption in agriculture ([Driving Agricultural Transformation: Unraveling Key Factors Shaping IoT Adoption in Smart Farming with Empirical Insights](https://www.mdpi.com/2071-1050/16/5/2129#:~:text=The%20Internet%20of%20Things%20,Moreover%2C%20financial)). Financial constraints and limited digital infrastructure were noted as barriers. The study provides empirical insights that reinforcing policy support, affordability, and training can significantly boost smart farming adoption in developing countries ([Driving Agricultural Transformation: Unraveling Key Factors Shaping IoT Adoption in Smart Farming with Empirical Insights](https://www.mdpi.com/2071-1050/16/5/2129#:~:text=Environment%E2%80%9D%20%28TOE%29%20factors,in%20research%20and%20practical%20applications)). (This offers context for all tropical countries, including how Malaysian smallholders might embrace IoT for crops like rice, oil palm, and coconut.) 41 | *Link:* [Sustainability Journal – Full Text](https://doi.org/10.3390/su16052129) (open access) 42 | 43 | ## Indonesia 44 | 45 | - **“Coconut Resilience: How Indonesia’s Farmers are Combating Climate Change”** (2024) – *Global Center on Adaptation (case study report)*. 46 | *Summary:* This report (blog-style case study) highlights a community-driven project in Yogyakarta, Indonesia, where coconut sugar farmers are adopting **climate-smart farming practices**. Led by a women-owned enterprise (Aliet Green), over 1,500 smallholder coconut farmers (90% women) have been empowered with simple, low-cost innovations to cope with extreme droughts and floods ([Coconut Resilience: How Indonesia’s Farmers are Combating Climate Change - Global Center on Adaptation](https://gca.org/coconut-resilience-how-indonesias-farmers-are-combating-climate-change/#:~:text=I%20n%20Indonesia%E2%80%99s%20Kulon%20Progo,food%20security%20and%20economic%20stability)) ([Coconut Resilience: How Indonesia’s Farmers are Combating Climate Change - Global Center on Adaptation](https://gca.org/coconut-resilience-how-indonesias-farmers-are-combating-climate-change/#:~:text=The%20enterprise%20is%20empowering%20local,easier%20to%20harvest%20for%20women)). Key measures include a “3R” water management system (Recharge-Retain-Reuse rainwater) to combat drought, and planting **dwarf coconut varieties** that are more climate-resilient and easier for women to harvest ([Coconut Resilience: How Indonesia’s Farmers are Combating Climate Change - Global Center on Adaptation](https://gca.org/coconut-resilience-how-indonesias-farmers-are-combating-climate-change/#:~:text=The%20enterprise%20is%20empowering%20local,easier%20to%20harvest%20for%20women)). The initiative blends traditional agroforestry with modern techniques to improve soil moisture, diversify crops, and secure livelihoods. Training in sustainable practices, pest management, and a shift to organic methods have improved yields and income stability for these farming communities. This case study exemplifies how digital and innovative solutions (early warning via mobile, adaptive technologies) are strengthening the resilience of coconut farmers in the face of climate change. 47 | *Link:* [Global Center on Adaptation – Case Study](https://gca.org/coconut-resilience-how-indonesias-farmers-are-combating-climate-change/) (free web article) 48 | 49 | ## Malaysia + Bangladesh 50 | 51 | - **"Sense-IT: An Aquaculture-Specific Autonomous Data Acquisition and Monitoring System"** - *IEEE (2022)*. 52 | *Abstract:* Aquaculture is now on an equal level with conventional fisheries in terms of economic impact. And by 2030, aquaculture will account for 60% of world fish consumption, while fishing will account for 40%. Aiming to enhance the process, a design of IoT-based real time data logger system for monitoring and visualizing water health by measuring associated water parameters of any aquaculture is presented in this work. The data logger, named Sense-IT is designed as a microcontroller-driven configuration where the program is developed in the C programming language. Water pH, Total dissolved solids (TDS), Electrical Conductivity (EC), Dissolved Oxygen (DO), Water Salinity, Oxidation-reduction potential (ORP), Dissolved ammonia, Florien, chlorine, and other parameters can be measured with this device. The microcontroller uses the RS-485 RTU communication protocol to connect with the sensors. This device collects all sensor data and sends it to the system's cloud server. This device is controlled and configured via a PHP Laravel-based web application that also allows the user to visualize the current status of the target water body. This device is adaptable since it can connect through Wi-Fi or GSM depending on the user's preference or network availability. The sensors provide robust accurate data and constantly monitors different environmental conditions of the target waterbody and records with a timestamp. 53 | *Link:* [IEEE DOI DOI: 10.1109/IES55876.2022.9888275](https://ieeexplore.ieee.org/document/9888275 ) 54 | 55 | -------------------------------------------------------------------------------- /SECURITY.md: -------------------------------------------------------------------------------- 1 | # Security Policy 2 | 3 | ## 📌 Overview 4 | 5 | This project is part of a community-led research initiative to improve coconut plantation yield through smart AgriTech and IT collaboration. While the project is open to all contributors, we take security and responsible disclosure seriously to protect the integrity of our data, research, and contributors. 6 | 7 | --- 8 | 9 | ## 🛡 Supported Versions 10 | 11 | We follow an open research and documentation model. The following areas are currently monitored for potential security concerns: 12 | 13 | - IoT device firmware or configurations (if published) 14 | - Cloud and backend service configurations (when applicable) 15 | - API endpoints or dashboards (when exposed) 16 | - Documentation or datasets that contain sensitive or identifiable information 17 | 18 | If you find any vulnerability in these areas, or in associated repositories, we kindly request that you follow the responsible disclosure process outlined below. 19 | 20 | --- 21 | 22 | ## 🧾 Reporting a Vulnerability 23 | 24 | If you have found a potential vulnerability, please do not create a public issue. 25 | 26 | Instead, report it privately via email: 27 | 28 | **📧 Email:** `damith@drklk.org` 29 | **🔒 Subject Line:** *Security Report – Coconut AgriTech Project* 30 | 31 | Please include: 32 | 33 | - A clear description of the issue 34 | - Steps to reproduce (if applicable) 35 | - Any relevant logs or screenshots 36 | - Suggested mitigation or fix (if known) 37 | 38 | We aim to acknowledge all genuine reports within **5 working days**. 39 | 40 | --- 41 | 42 | ## 🔒 Responsible Disclosure 43 | 44 | We greatly appreciate responsible researchers who take the time to report issues privately and allow us to address them properly before public disclosure. 45 | 46 | Please do not: 47 | 48 | - Exploit vulnerabilities to gain unauthorised access 49 | - Share vulnerabilities publicly before we have confirmed and responded 50 | - Use the project or its data to conduct testing without permission 51 | 52 | --- 53 | 54 | ## ✅ Our Commitment 55 | 56 | We will: 57 | 58 | - Acknowledge your report promptly 59 | - Keep you informed of the status and resolution 60 | - Credit your contribution (unless you prefer to remain anonymous) 61 | - Work with urgency to patch or document fixes 62 | 63 | --- 64 | 65 | ## 👥 Community Safety 66 | 67 | If you come across behaviours or contributions that may compromise user safety or violate ethical norms (e.g., unauthorised data collection, harmful configurations, etc.), please report them to the same email address above. 68 | 69 | --- 70 | 71 | *Document last updated: {{21st April 2025}}* 72 | 73 | -------------------------------------------------------------------------------- /Sensors.md: -------------------------------------------------------------------------------- 1 | # List of sensors available for evaluation 2 | 3 | ### 📡 Smart Agriculture IoT Devices & Renewable Energy Tools 4 | 5 | | Device Type | Model / Name | Sourcing URL | Rough Price (USD) | 6 | |------------------------|-------------------------------------|------------------------------------------------------------------------------|-------------------| 7 | | Soil Moisture Sensor | Capacitive Soil Moisture Sensor v1.2 | [AliExpress](https://www.aliexpress.com/item/32761793117.html) | $1 – $3 | 8 | | Soil pH Sensor | Gravity Analog pH Sensor (DFRobot) | [DFRobot](https://www.dfrobot.com/product-1782.html) | $25 – $35 | 9 | | Temperature & Humidity | DHT22 / AM2302 | [Amazon](https://www.amazon.com/dp/B01DKC2GQ0) | $5 – $10 | 10 | | Weather Station | WH-SP-WS01 / WH-SP-WS02 | [AliExpress](https://www.aliexpress.com/item/1005003237207287.html) | $70 – $120 | 11 | | Light Sensor (LUX) | BH1750 Light Intensity Sensor | [Amazon](https://www.amazon.com/dp/B010N1SPRK) | $3 – $6 | 12 | | Camera Module | Raspberry Pi Camera Module v2 | [Raspberry Pi Official](https://www.raspberrypi.com/products/camera-module-v2/) | $30 | 13 | | Microcontroller Board | ESP32 DevKit | [AliExpress](https://www.aliexpress.com/item/32809370800.html) | $5 – $10 | 14 | | Microcontroller Board | Arduino Uno R3 | [Arduino.cc](https://store.arduino.cc/products/arduino-uno-rev3) | $25 – $30 | 15 | | Communication Module | SX1278 LoRa Module | [AliExpress](https://www.aliexpress.com/item/32829301342.html) | $5 – $10 | 16 | | Gateway (DIY LoRa) | RAK7240 WisGate Edge Lite | [RAKwireless](https://store.rakwireless.com/products/rak7240) | $150 – $250 | 17 | | Solar Panel | 10W 12V Solar Panel (Polycrystalline) | [AliExpress](https://www.aliexpress.com/item/4001213225791.html) | $20 – $30 | 18 | | Solar Panel Kit | 30W Solar Panel + Controller + Battery | [Amazon](https://www.amazon.com/dp/B08D6YFR81) | $50 – $70 | 19 | | Lithium Battery Pack | 18650 3.7V 2500mAh Rechargeable | [AliExpress](https://www.aliexpress.com/item/32859407864.html) | $2 – $5 each | 20 | | Charge Controller | TP4056 Module for 18650 | [AliExpress](https://www.aliexpress.com/item/32831261735.html) | $0.50 – $1 | 21 | | Enclosure (Weatherproof) | IP65 ABS Junction Box | [Amazon](https://www.amazon.com/dp/B07MT9V5W1) | $10 – $20 | 22 | | Drone (for imagery) | DJI Mini 2 SE | [DJI](https://www.dji.com/mini-2-se) | $339 | 23 | | AI Edge Computing | Raspberry Pi 4 (4GB) | [Raspberry Pi Official](https://www.raspberrypi.com/products/raspberry-pi-4-model-b/) | $45 – $60 | 24 | 25 | --- 26 | 27 | ## Online Platforms for Smart Agri-Tech & IoT Devices 28 | 29 | The following are reputable online platforms/stores (global and regional) where users can explore or purchase sensors, modules, weather stations, and other smart agri-tech IoT devices: 30 | 31 | | **Store Name** | **Type of Devices/Focus** | **Region Based** | **URL** | 32 | |----------------------|-------------------------------------------------------|------------------------|-------------------------------| 33 | | **Seeed Studio** | IoT and maker electronics (open-source hardware, sensor modules, LoRaWAN nodes, etc.), including dedicated **smart agriculture kits** (SenseCAP series for soil, weather, livestock monitoring) ([about seeed - Latest News from Seeed Studio](https://www.seeedstudio.com/blog/forum-2/about-seeed/?srsltid=AfmBOoqnH7_w4OrOC2r3YFFXzU0VzRDzR2QDvX3vCGUtxYLKDCBL8nFB#:~:text=Seeed%20has%20been%20serving%20the,for%20IoT%2C%20edge%20AI%20applications)). Great for prototyping and ready-to-deploy sensor solutions. | China (Global shipping) | **seeedstudio.com** | 34 | | **SparkFun** | DIY electronics retailer offering a wide array of **sensors, microcontrollers, and IoT kits** for projects. Sells “bits and pieces” for electronics projects ([SparkFun Electronics](https://www.sparkfun.com/#:~:text=SparkFun%20is%20an%20online%20retail,make%20your%20electronics%20projects%20possible)) – e.g. soil moisture sensors, weather station parts, Arduino boards – popular among makers and researchers. | USA (Global) | **sparkfun.com** | 35 | | **Adafruit Industries** | Open-source hardware store with **sensors, modules, and DIY kits** (temperature, humidity, GPS, etc.), plus tutorials. Known for easy-to-use breakout boards and **environmental sensor kits** useful in smart farming. Caters to hobbyists and educators for global shipments. | USA (Global) | **adafruit.com** | 36 | | **Libelium IoT Marketplace** | One-stop online store by Libelium offering **fully integrated IoT solution kits** for agriculture and environment. Features ready-to-deploy bundles like Smart Agriculture sensors (soil moisture, weather, irrigation control) with data gateways and cloud software. Targets professional deployments (e.g. farms, vineyards). | Spain (Global) | **theIoTMarketplace.com** *(Libelium)* | 37 | | **Davis Instruments** | Manufacturer’s store for **professional weather stations and sensors**. Offers products like Vantage Vue/Pro2 weather stations, soil moisture and temperature sensors, and data loggers, widely used in agriculture for microclimate monitoring. A *global leader in weather monitoring equipment* ([Weather Stations - Davis Instruments](https://www.davisinstruments.com/collections/weather-stations?srsltid=AfmBOoprp6WCP-KgA998HWd84LdAAehqd5K3Lqqjgrv6zuLMUsvKrJS6#:~:text=Davis%20Instruments%20delivers%20professional%20weather,and%20Vantage%20Pro2%20models)) with distributors worldwide. | USA (Global) | **davisinstruments.com** | 38 | | **AliExpress** | Large e-commerce marketplace hosting many vendors of **affordable IoT components** – from soil moisture probes, water pumps, Arduino sensors to full weather station kits. Useful for sourcing low-cost modules for agri-tech prototypes (users should vet quality). Worldwide shipping, based in China. | China (Global) | **aliexpress.com** | 39 | | **Digi-Key Electronics** | Online distributor of **electronic components** with an extensive catalog of sensors, wireless modules, and dev boards. Farmers and developers can find everything from industrial soil sensors to IoT radios (LoRa, GSM) from top brands. Known for fast global shipping and inventory. | USA (Global) | **digikey.com** | 40 | 41 | 42 | ### ⚠️ Disclaimer 43 | 44 | - **Prices are approximate** and may vary based on supplier, region, shipping, and availability. 45 | - The **sourcing URLs are examples** for public access and do not represent endorsements. 46 | - This list is provided for **educational and prototyping purposes only**. 47 | - Please validate compatibility and durability for long-term outdoor deployment. 48 | - Prices last checked: *April 2025*. 49 | --------------------------------------------------------------------------------