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Enhancing Agricultural Practices and Food Security
Abstract
This paper delves into the enhancements in agricultural practices and the bolstering of food security facilitated by AI-driven engineering. Through case studies and research insights, it scrutinizes how artificial intelligence is revolutionizing traditional agricultural methods, including crop monitoring, pest control, and yield prediction. The study underscores the application of AI techniques such as remote sensing, drone technology, and precision agriculture in optimizing resource allocation, mitigating risks, and increasing productivity in farming. Additionally, it discusses the integration of AI with IoT devices, weather forecasting systems, and crop modeling to enable real-time decision-making and adaptive management of agricultural operations. The paper also addresses challenges such as data interoperability, farmer adoption, and environmental sustainability in the deployment of AI-driven engineering solutions in agriculture. It emphasizes the necessity for interdisciplinary collaboration, policy support, and knowledge transfer to harness AI's potential in ensuring global food security and sustainable agriculture.
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References
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