AI & Environment Resource HubFarming, food systems, soil, and agricultural resource use.
Browse all 95 resources →Counts reflect provisional metadata classification, not measured environmental impact. A record may belong to multiple topics.
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Research papers and scholarly literature on impacts, applications, and methods.
AI enhances food and agriculture by optimizing supply chains, improving quality, and ensuring safety, but its adoption faces challenges related to data privacy, automation-driven job displacement, and ethical concerns in sustainability and regulation compliance.
AI enhances precision agriculture through data-driven crop management, irrigation optimization, and pest control, but gaps remain in nutrient management and sensor integration.
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.
AI-powered drones equipped with advanced imaging and edge computing significantly enhance precision agriculture in cashew farming by enabling early disease detection and optimized pesticide application, achieving up to 95% accuracy in anthracnose classification and 99% accuracy in identifying healthy leaves, thereby improving sustainability, productivity, and resource efficiency in developing regions.
Missing metadata is a curation task, not evidence of absent research or activity. The Resource Hub does not currently contain a validated incident dataset, expert survey, or measurements of net environmental benefit.