AI-Driven Smart Agriculture for Climate-Resilient Farming
Abstract
Climate change poses significant risks to agricultural productivity, requiring innovative solutions for resilient farming. This paper explores machine learning techniques for climate-smart agriculture, including AI-driven crop disease detection, soil moisture prediction, and weather-adaptive irrigation. Techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning are applied to optimize farming practices and reduce resource wastage. Case studies highlight AI-powered precision agriculture initiatives that enhance food security and promote sustainability in the face of climate uncertainty.
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