Enhanced Prediction of Jasmine 105 Rice Growth with RC-ELM and Slow-Release Organic Fertilizers

This study explores the use of a Residual Compensation Extreme Learning Machine (RC-ELM) to predict the growth of Jasmine 105 rice, specifically in the context of slow-release organic fertilizers (SROFs). The experiment involved four types of fertilizers: Cow Manure, Filter Cake, Aerated Compost, and a standard chemical control (27-12-6). The macronutrient content of each fertilizer was used as key input variables in the RC-ELM model, with real-time field sensor data providing insights. After extensive preprocessing through normalization and feature engineering, RC-ELM demonstrated superior performance compared to traditional models, such as Linear Regression, Support Vector Machines (SVM), and standard ELM variants. In particular, RC-ELM achieved an R2 = 0.9609, Y=14.982x 103.58 for Aerated Compost, reducing the Mean Squared Error (MSE) by 30%. The results indicate that while organic fertilizers like Aerated Compost may incur higher costs, they offer long-term sustainability benefits, including improved soil fertility. The study further highlights the importance of adopting organic agricultural practices, which align with internationally recognized standards, such as Organic Thailand and IFOAM, for food safety and environmental preservation. These findings underscore the potential of RC-ELM in enhancing crop yield predictions while supporting sustainable farming practices.