STM Journals Peer-reviewed research from Consortium e-Learning Network

Research & Reviews : Journal of Agricultural Science and Technology Review Article

Advancements in Agricultural Forecasting: A Review of Machine Learning Based Crop Yield Prediction

  1. VijayLaxmi Faculty of computing &GuruKashiUniversity,TalwandiSabo
  2. GauravSingla Faculty of computing &GuruKashiUniversity,TalwandiSabo

Abstract

Agricultural productivity plays a critical role in global food security, and accurate crop yield prediction is essential for optimizing resource allocation and decision-making in farming. The rapid advancements in Machine Learning (ML) and Deep Learning(DL)have transformed agricultural forecasting, enabling data-driven approaches for crop prediction. This review paper provides a comprehensive analysis of various ML and DL techniques applied in crop yield forecast, highlighting the ineffectiveness, challenges, and future directions. The study explores different models, including Random Forest, Support Vector Technologies, Artificial Neural Networks, Long Short-Term Memory Networks, and other ensemble methods, comparing the performance based on accuracy metrics such as R² score, RMSE,and MAE. Additionally, the paper discusses the incorporation of IoT and remote sensing technologies in modern precision agriculture.statistical methods, while deep learning models excel in handling complex, nonlinear relationships in agricultural data. However, challenges such as data availability,environmental variability,and computationale fficiency remain key barriers.This review aims to provide insights into the potential of AI-driven approaches inenhancing agricultural sustainability and precision farming,paving the way for future research and innovations in smart agriculture

Keywords

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