Sustainable Agriculture with AI: Climate-Resilient Crop Management Predictive Models

Abstract

The agricultural sector is facing significant challenges as a result of climate change, necessitating innovative solutions to ensure sustainable practices and boost crop resilience. agricultural sustainability solutions driven by artificial intelligence, with a focus on developing climate-resilient models for crop management prediction. The optimal planting times, watering amounts, and pest management strategies are determined by these prediction models thru the analysis of extensive data sets, which include historical yields, weather patterns, soil quality, crop features, and data analytics powered by machine learning algorithms. utilizing real-time data collected thru internet of things (IoT) devices and remote sensing technologies; this data, when coupled with AI models, aids farmers in making more informed decisions. The successful use of AI-based predictive models in various agricultural contexts has been established thru case studies, which improve output, resource efficiency, and environmental sustainability. Problems with data availability, technical infrastructure, and farmer education are preventing widespread adoption; resolving these issues will need concerted effort from all stakeholders. The ultimate goal of AI-driven solutions is to increase global food production and make farming practices more resistant to climate change.