Particle Swarm Optimization-Random Forest Weather-Based Crop Yield Prediction Model

Abstract

Crop production is a vital source of food for humans, and improving crop yield requires a deep understanding of crop production processes. It has been proven that increasing crop yield reduces poverty, crop failure risk, increases productivity, and optimizes the value of agricultural land. Many factors affect the amount of crop harvested in a specific area and several studies, mainly in the agricultural context, have been conducted to estimate crop yield production with Machine learning (ML) techniques. This study explores five cereal crop yields: rice, maize, wheat, sorghum, and soybeans with Particle Swarm Optimization (PSO) and Random Forest prediction approaches. Performance metrics such as R2 score, Mean Absolute Error, and Root Mean Squared Error confirm the authenticity of the model. The result of the optimized Crop yield prediction has an R2 score of 97.13, MAE of 124.75, and RMSE of 1273.73. The model performed better than other existing approaches, such as Random Forest (RF) and Decision Tree (DT). This study will provide farmers with reliable crop yield predictions, enabling better planning based on weather conditions.