Optimizing Agriculture with Intelligent Solutions
Abstract
This paper explores the optimization of agriculture through intelligent solutions enabled by AI-driven engineering. Through case studies and research insights, it investigates how artificial intelligence is revolutionizing traditional agricultural practices, including crop monitoring, yield prediction, and precision farming. The study highlights the application of AI techniques such as machine learning, remote sensing, and drones in enhancing productivity, sustainability, and resource efficiency in agriculture. Additionally, it discusses the integration of AI with IoT sensors, satellite imaging, and weather forecasting models to enable smart irrigation, pest detection, and crop management decisions. The paper also addresses challenges such as data interoperability, farmer adoption, and ethical considerations in the adoption of AI-driven engineering solutions in agriculture. It emphasizes the importance of interdisciplinary collaboration, data sharing, and farmer-centric innovation in leveraging AI's potential to address global food security challenges and build resilient agricultural systems.
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References
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