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Revolutionizing Agricultural Practices for Sustainable Food Production
Abstract
This paper explores the revolutionizing impact of AI-driven engineering on agricultural practices for sustainable food production. Through case studies and research insights, it investigates how artificial intelligence is reshaping traditional farming methods, including crop monitoring, yield prediction, and resource management. The study highlights the application of AI techniques such as remote sensing, predictive modeling, and precision agriculture in optimizing agricultural productivity, reducing environmental impact, and ensuring food security. Additionally, it discusses the integration of AI with drones, IoT sensors, and farm machinery to enable real-time monitoring, data-driven decision-making, and autonomous operations in agriculture. The paper also addresses challenges such as data access, farmer adoption, and rural connectivity in the deployment of AI-driven engineering solutions in agriculture. It emphasizes the importance of interdisciplinary collaboration, policy support, and capacity building in leveraging AI's potential to transform agricultural practices and address global food challenges.
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