AI & Environment Resource HubFarming, food systems, soil, and agricultural resource use.
Browse all 124 resources →Counts reflect provisional metadata classification, not measured environmental impact. A record may belong to multiple topics.
Share of the 124 records in this view. Pathways can overlap; bars do not add to 100%.
Research papers and scholarly literature on impacts, applications, and methods.
The CRE.AI.TIVE platform uses machine learning to enhance plant gene regulation, enabling scalable gene upregulation without prior knowledge of regulatory elements.
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.
This study highlights the impact of climate change on water balance in the Upper Bhima River, revealing reduced monsoon precipitation and baseflow sensitivity, with implications for agriculture, biodiversity, and water management.
AI enhances precision agriculture through data-driven crop management, irrigation optimization, and pest control, but gaps remain in nutrient management and sensor integration.
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.