Machine Learning Algorithm for Optimal Yield Prediction of Cowpea (An IoT Smart Farming Approach)

Abstract

Cowpea (Vigna unguiculata) is a vital legume crop valued for its nutritional benefits and role in enhancing soil fertility; however, traditional farming practices often result in inconsistent yields due to environmental stresses and inefficiencies. This study explores how integrating Internet of Things (IoT), smart farming, and machine learning (ML) can optimize cowpea yield prediction and promote sustainable agriculture. The research focuses on implementing IoT-enabled smart farming systems with ML algorithms specifically Random Forest and AdaBoost to improve yield forecasting. IoT sensors were deployed to collect real-time data on critical parameters such as soil moisture, temperature, and nutrient levels, which were then used to train the predictive models. Performance evaluation using MAE, MSE, RMSE, and R² metrics revealed that Random Forest achieved perfect predictive accuracy (MAE, MSE, RMSE = 0.00; R² = 1.00), indicating strong generalization capability, while AdaBoost performed slightly less accurately (MAE = 0.05; MSE = 0.01; RMSE = 0.09; R² = 0.75), suggesting high accuracy but potential overfitting. The findings underscore the importance of soil nutrients and environmental variables in yield prediction and demonstrate that integrating IoT, smart farming, and ML particularly Random Forest holds great promise for advancing precision agriculture, increasing productivity, and fostering sustainable farming practices.