AI-DRIVEN APPROACHES TO CROP YIELD PREDICTION AND MANAGEMENT: TOWARDS SUSTAINABLE AND SMART AGRICULTURE"

Abstract Climate change, soil degradation, and pest outbreaks are increasingly threatening agricultural productivity, heightening the need for accurate crop-yield forecasting. Artificial intelligence (AI) – leveraging satellite imagery, sensor networks, and farm data – offers powerful new tools to predict yields with high precision. This paper synthesizes current challenges and solutions in AI-driven yield prediction. We review how machine learning (ML) and deep learning models (e.g. Random Forests, gradient boosting, convolutional neural networks, recurrent networks) have been applied to large multi-modal datasets (satellite VIs, weather, soil sensors, farm records) to forecast yields . Through case studies (e.g. winter wheat in China, sugarcane in Brazil, mixed crops in Ethiopia), we illustrate how AI models (LSTM, RF, GA regression) can achieve high accuracy (e.g. R² up to 0.92 or RMSE reductions) by integrating time-series and spatial data. We identify critical barriers, including limited labelled data, rural connectivity and infrastructure gaps, and the opaque “black-box” nature of AI models. We propose solutions such as federated learning for privacy-preserving multi-farm training, edge computing to enable on-device analytics, transfer learning to adapt models across regions, and explainable AI (XAI) to build trust. We also discuss future directions like climate-resilient modeling, farm digital twins, and inclusive tools for smallholder farmers. The paper concludes with recommendations to bridge AI research and practical deployment, emphasizing scalable data strategies and user-friendly decision-support platforms.