Smart Agriculture

Standard Blurb: The book integrates multiple technologies, including machine learning, computer vision, and the Internet of Things, to provide a comprehensive guide for smart farming. It covers advanced topics like GANs, vision transformers, and real-time data analytics, providing a cutting-edge resource for researchers and practitioners. Emphasizes real-world applications through case studies on precision farming, pest detection, and crop yield prediction. Explores computer vision techniques critical to farming, including image preprocessing (noise reduction, augmentation), feature extraction, and traditional methods like HSV thresholding. Discuss topics that include NDVI calculation, anomaly detection in satellite time-series data, and drone-based multispectral imaging. Examines computer vision applications in animal husbandry, such as thermal imaging for fever detection, YOLO-based tracking for grazing patterns, and 3D pose estimation for lameness detection. Covers the integration of artificial intelligence with robotics for tasks like autonomous weeding, fruit picking, and precision spraying. The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communication engineering, computer science and engineering, agricultural engineering, and crop science.