Sustainable farming with machine learning solutions for minimizing food waste

Abstract

This research explores the application of Artificial Intelligence (AI) and Machine Learning (ML) in mitigating post-harvest losses and reducing food waste within the agricultural supply chain. Our objective is to rigorously quantify the effectiveness of these technologies at various stages of food handling, from production to consumption, to improve food security and sustainability. The study employs a mixed-methods approach, integrating quantitative data from IoT sensors deployed in field studies and qualitative insights from stakeholders, including farmers and retailers. The study's findings reveal that AI-driven cold storage interventions led to a 60% reduction in post-harvest losses for perishable items. Meanwhile, ML-optimized logistics resulted in a 20% decrease in transportation-related food waste. Despite these improvements, challenges remain in accurately predicting market demands, occasionally leading to overproduction. This highlights the need for further refinement in AI algorithms to handle market volatility. Integrating AI and ML in agricultural practices offers substantial benefits, demonstrating the potential to transform food supply chain management. However, additional improvements are required to maximize accuracy and efficiency. Future applications of the models include real-time adaptive logistics, blockchain integration for traceability, and AI-powered predictive demand forecasting.