Enhancing the livelihoods of small-scale paddy farmers in sri lanka through computational approaches for inclusive and equitable agriculture

Abstract

Paddy cultivation, also known as rice cultivation, contributes significantly to Sri Lanka's food security and economic prosperity. Nevertheless, a number of challenges exist in the practice, such as insufficient furrow irrigation techniques, inefficient fertilizer subsidy distribution, a lack of yield prediction models, and limited access to research findings. In order to address these concerns, this research proposes an integrated system with four components. An application for furrow irrigation planning is included in the first component, utilizing support vector machine algorithms and random forest algorithms to optimize irrigation paths. Second, a priority-based fertilizer subsidy system is introduced, employing Gradient Boosting to allocate subsidies efficiently based on farmers' needs. The third component focuses on improving the dissemination of research findings by employing text summaries in order to make the findings more accessible to small-scale farmers. In the fourth component, a yield prediction model is implemented using a Random Forest algorithm that takes into account climatic and soil variables in order to forecast expected yields. The integrated system aims to enhance the efficiency, productivity, and profitability of paddy farming in Sri Lanka, providing practical solutions to challenges faced by farmers. By adopting these innovations, farmers can make informed decisions and optimize their agricultural practices, leading to sustainable rice production and economic growth.