Advancing Agricultural Sustainability: Machine Learning-Driven Insights for Optimized Crop Yield Forecasting

Sustainable agriculture is crucial for addressing global challenges related to food security, resource efficiency, and environmental preservation. This study examines the potential of machine learning (ML) in forecasting crop production to enhance decision-making and promote sustainable agricultural practices. The research investigates advanced machine learning techniques, including Multilayer Perceptron Artificial Neural Networks (MLP ANN), Decision Trees, Voting Classifiers, and YOLO models, for accurate agricultural yield prediction. These models employ several data sources, encompassing climatic conditions, soil properties, and agricultural practices. Among the assessed models, the MLP ANN achieved a notable accuracy of 91%, illustrating its efficacy in predicting agricultural production. The results demonstrate how these predictive models aid farmers and policymakers in optimizing resource allocation, reducing waste, and aligning agricultural inputs with expected outputs, thereby decreasing environmental impact and improving economic outcomes. Essential insights highlight the effectiveness of data-driven methods in reducing uncertainty and enhancing agricultural productivity. The paper finishes by identifying barriers to the widespread adoption of ML-based solutions and outlining opportunities for integrating these technologies into sustainable agricultural systems, so offering a framework for future implementation.