International Journal of Multi-Disciplinary Research and Developmental Studies
E-ISSN: 2469-7811 • Impact Factor - 9.587

AI for Climate Change and Smart Agriculture

Abstract

Artificial Intelligence (AI) transforms Climate-Smart Agriculture (CSA) by combining real-time data, machine learning, and IoT sensors to boost farm productivity while lowering environmental impact. Core Applications of AI in Smart Agriculture • Precision Resource Management: Variable-rate technology and AI algorithms analyze soil data, weather, and crop needs to cut water use by up to 40% and reduce pesticide application by 30%. • Yield & Climate Prediction: On-device machine learning models and neural networks (such as Random Forest classifiers and physics-informed models) forecast crop yields and local weather changes with up to 90% accuracy. • Pest & Disease Early Warning: Computer vision models (like YOLO and vision transformers) process smartphone photos of crops or satellite indices (NDVI) to detect pest outbreaks and plant diseases in seconds. • Integrated Agriculture Intelligence Systems: Real-time platforms like India's Fieldwise deliver localized mobile alerts and live monitoring to millions of farmers, helping protect vulnerable crops ahead of extreme weather events. • Soil Health & Carbon Sequestration: Remote sensing and soil sensors track organic carbon stocks and support regenerative practices like crop rotation and cover cropping.

Keywords: AI, climate change, smart agriculture, computer vision, technology

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