Computer Vision and Machine Learning in Agriculture, Volume 2

This book provides an overview of advanced machine learning and deep learning tools and techniques for solving agricultural plants and products problems, with a focus on crops harvesting, weed, multi-class crops detection, yield estimation, crop maturity detection, agri-food product quality assessment, and medicinal plant recognition.

Format: Hardback
Length: 260 pages
Publication date: 14 March 2022
Publisher: Springer Verlag, Singapore

This book serves as an extension of the previous publication "Computer Vision and Machine Learning in Agriculture," catering to the needs of academicians, researchers, and professionals engaged in addressing the challenges associated with agricultural plants and products to enhance production through the integration of advanced machine learning techniques, including deep learning tools and methodologies, into computer vision algorithms. Comprising a total of 15 chapters, the book begins by delving into the realm of crop harvesting, weed, and multi-class crops detection utilizing robots and Unmanned Aerial Vehicles (UAVs) through the application of machine learning and deep learning algorithms for smart agriculture. The subsequent two chapters focus on the retrieval and collection of agricultural data. Chapters 6, 7, 8, and 9 specifically emphasize yield estimation, crop maturity detection, agri-food product quality assessment, and medicinal plant recognition, respectively. The remaining six chapters are dedicated to the optimization of disease recognition through computer vision-based machine and deep learning strategies.

This comprehensive resource offers valuable insights and practical applications for those involved in the field of agriculture, enabling them to leverage the power of computer vision and machine learning to improve crop yields, reduce waste, and enhance the quality of agricultural products. By leveraging the latest advancements in these technologies, the book empowers researchers and practitioners to address the complex challenges faced by the agricultural industry and drive sustainable growth and development.

Weight: 582g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811699900
Edition number: 1st ed. 2022