Title:AI-Powered Crop Yield Prediction and Optimization


Authors:Moksha Jain, Mitushi Tawar, Nidhi Nigam, Vandana Kate


Published in: Volume 3 Issue 1 Jan June 2026, Page No.280-284


Keywords:Artificial Intelligence (AI), Internet of Things (IoT), Crop Yield Prediction, Precision Agriculture, Explainable AI (XAI), Machine Learning, Sustainable Farming, Cloud-based Decision Support.


Abstract:India’s agriculture challenges—unpredictable weather, soil degradation, and pest outbreaks—making yields uncertain. experience-based farming can no longer ensure food security or sustainability in today’s changing climate.An intelligent, data-driven approach is proposed to enhance agricultural productivity by integrating artificial intelligence with real-time environmental data. This approach utilizes advanced learning models to predict crop yield, optimize resource utilization, and generate explainable, localized recommendations. Advances in AI and IoT are transforming agriculture through real-time monitoring and intelligent analytics. By integrating sensor, weather, and satellite data with advanced learning models, this approach predicts crop yields, optimizes irrigation and resource use, and delivers explainable, recommendationsIntegrating live field data with intelligent analytics, AI-driven frameworks enable precision farming by boosting productivity, reducing waste, and empowering farmers with adaptive, region-specific, and sustainable decision support. This framework promotes sustainable, efficient, and informed farming practices suited to conditions.AI-based yield prediction frameworks have the potential to transform agriculture into a more efficient, transparent, and resilient ecosystem.


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