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Advancing Agricultural Productivity for Food Security
Abstract
This paper explores the advancement of agricultural productivity for food security through AI-driven engineering. Through case studies and research insights, it investigates how artificial intelligence is revolutionizing traditional agricultural practices, including crop monitoring, pest management, and yield prediction. The study highlights the application of AI techniques such as satellite imagery analysis, precision agriculture, and crop modeling in optimizing resource utilization, reducing environmental impact, and increasing crop yields. Additionally, it discusses the integration of AI with IoT devices, drones, and farm management software to enable real-time monitoring, automated decision-making, and data-driven farming practices. The paper also addresses challenges such as data interoperability, technology adoption, and farmer education in the deployment of AI-driven engineering solutions in agriculture. It emphasizes the importance of collaboration among researchers, policymakers, and farmers in harnessing AI's potential to enhance agricultural productivity, ensure food security, and promote sustainable farming practices.
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