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Advancing Agricultural Automation for Sustainable Food Production
Abstract
This paper delves into the advancements made in agricultural automation for sustainable food production through AI-driven engineering. It explores how artificial intelligence is reshaping traditional agricultural practices, including planting, irrigation, and pest control. Through case studies and research insights, the study highlights the application of AI techniques such as computer vision, robotics, and precision agriculture in optimizing resource utilization, increasing yield, and minimizing environmental impact in farming. Additionally, it discusses the integration of AI with Internet of Things (IoT) sensors, drones, and autonomous machinery to enable real-time monitoring, data-driven decision-making, and autonomous operation in agricultural systems. The paper also addresses challenges such as data interoperability, rural connectivity, and farmer adoption in the deployment of AI-driven engineering solutions in agriculture. It emphasizes the importance of interdisciplinary collaboration, farmer education, and policy support to leverage AI's potential in revolutionizing agriculture and ensuring food security for a growing global population.
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