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Artificial Intelligence in Agricultural Systems: Fundamentals and Advances focuses on advancements in AI in and draws upon the multi-faceted challenges of modern agriculture. This book curates the latest strands of AI applications that are reshaping agricultural systems, including current trends and emerging technologies. It offers an overview of the evolving agricultural landscape and foregrounds the critical role of AI in driving innovation and enhancing productivity in this essential sector. The book is organized into four parts, starting with an introduction to agricultural systems and the fundamentals of AI, including machine learning, deep learning, natural language processing, and robotics. It then pivots into data collection and processing techniques tailored to agriculture, and explicates on the use of sensors, IoT devices, remote sensing, and UAVs. Core applications of AI in crop and livestock management are covered, with special emphasis on yield prediction, pest detection, precision irrigation, and sustainable farming practices. The final sections address the limitations of AI in agriculture, future trends, and policy recommendations, providing an overarching understanding of AI's responsible adoption. Artificial Intelligence in Agricultural Systems is an important resource for upper-level undergraduate and graduate students studying the impact of AI technologies on agriculture and food production. Researchers, practitioners, and policymakers studying the intersections of agriculture, environmental science, data science, and engineering will find this book compelling as well. Practical applications and supplementary materials such as case studies, data sets, and a question bank further enhance the learning experience. These provide readers with the tools and knowledge necessary to chart global food demand and effectively handle the environmental challenges of the future.
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