AI & Environment Resource HubOlawale et al. · 2025-05-01
This study examines how AI and ML can reduce food waste in the agricultural supply chain, demonstrating significant reductions in post-harvest losses and transportation waste while highlighting the need for further optimization in market demand prediction.
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Olawale et al.. 2025-05-01. Sustainable Farming With Machine Learning Solutions for Minimizing Food Waste. https://www.sciencedirect.com/science/article/pii/S0022474X25000700 (AI & Environment Resource Hub; record paper-145; collection snapshot 2026-10-04).
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Source sheet: Scientific Paper · Row 149 · Original ID: paper-145.
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Shared topics: Agriculture and food
This study develops a machine vision and deep learning framework, optimized with EfficientNetB7 and the Lion optimizer, that accurately identifies consumed food products across 32 categories, achieving up to 100% classification accuracy and supporting sustainable food consumption goals.
Shared topics: Agriculture and food
This article offers practical guidelines for applying deep learning to tabular agricultural data, using a cereal yield forecasting case study to illustrate best practices in model selection, data preprocessing, and cross-validation for new users tackling agronomic problems.
Shared topics: Agriculture and food
AI enhances precision agriculture through data-driven crop management, irrigation optimization, and pest control, but gaps remain in nutrient management and sensor integration.
Shared topics: Agriculture and food
Abeya is providing chocolate companies with cacao sourced directly from cacao cooperatives transitioning to regenerative practices. We are scaling regenerative agriculture by supporting tropical farmers with new technology and better prices. For farmers, the planet, and the companies who produce the products we love.