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Transforming Agricultural Practices for Sustainable Food Production
Abstract
This paper explores the transformation of agricultural practices for sustainable food production through AI-driven engineering. Through case studies and research insights, it investigates how artificial intelligence is revolutionizing traditional farming methods, including crop monitoring, pest management, and yield prediction. The study highlights the application of AI techniques such as remote sensing, precision agriculture, and predictive analytics in optimizing resource allocation, minimizing environmental impact, and increasing crop yields. Additionally, it discusses the integration of AI with drones, sensors, and smart farming equipment to enable real-time monitoring, decision support, and autonomous operations in agriculture. The paper also addresses challenges such as data interoperability, farmer adoption, and rural connectivity in the deployment of AI-driven engineering solutions in agriculture. It emphasizes the importance of knowledge sharing, capacity building, and stakeholder engagement in leveraging AI's potential to enhance agricultural productivity, profitability, and sustainability.
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