Machine Learning and Deep Learning Approaches for Accurate Crop Yield Prediction

Abstract

Crop yield prediction plays a crucial role in modern agriculture by helping farmers, policymakers, and agricultural industries make informed decisions regarding crop management, resource allocation, and food security. Accurate prediction of crop yield can reduce economic losses and improve productivity by enabling early planning and efficient utilization of agricultural resources. This project presents a comparative study of three machine learning and deep learning techniques — Random Forest (RF), Feed Forward Neural Network (FFNN), and Recurrent Neural Network (RNN) — for predicting crop yield based on historical agricultural and environmental data.The proposed system utilizes datasets containing factors such as rainfall, temperature, humidity, soil conditions, fertilizer usage, and previous crop production records. Data preprocessing techniques including cleaning, normalization, and feature selection are applied to improve model performance and prediction accuracy. The Random Forest algorithm is employed for its robustness and ability to handle nonlinear relationships within the data. The Feed Forward Neural Network is used to learn complex feature interactions through multiple hidden layers, while the Recurrent Neural Network is implemented to capture temporal patterns and sequential dependencies present in historical agricultural data.