Integration of Machine Learning for Performance Analysis and Optimization of a Teaching Model Rice Threshing Machine

Abstract Rice is a indispensable food crop in Nigeria, yet domestic production lingers to decrease as demand increases, based majorly on post-harvest losses credited to ineffective threshing approaches. This investigation x-rayed the fabrication and performance optimization of a laboratory scale prototype of a rice threshing machine, fabricated by means of locally sourced materials to certify cost-effectiveness and ease of maintenance for local farmers. The system encompasses a welded steel frame, hopper, peg-tooth threshing drum, blower, grain and chaff outlets, and a V-belt driven electric motor. To permit data driven optimization, an Internet of Things (IoT) based sensing panel was integrated, integrating sensors for drum speed, paddy moisture content, feed rate, and throughput capacity. Operational data were gathered under changeable settings of drum speed, feed rate, pulley ratio, and grain moisture content. Three machine learning (ML) algorithms; Random Forest Regression, Support Vector Regression, and Artificial Neural Networks were trained to determine threshing efficiency, grain breakage, and energy consumption. The Random Forest model succeeded in having the maximum forecast accuracy (R² = 96.4%, RMSE = 1.25) and was employed to ascertain optimal operating factors. Machine learning supported process enhanced threshing efficiency by 5.2%, decrease grain breakage by 22.4%, and dropped energy depletion by 9.8% compared to manual variations. The outcome established that incorporating machine learning based forecasting modeling with locally constructed threshing machines can significantly improve performance, reduce post-harvest losses, and encourage sustainable agricultural mechanization in resource restricted situations.