With rapid progress being made in both theory and practical applications, Artificial Intelligence (AI) is transforming every aspect of life and leading the world towards a sustainable future. AI technology is fundamentally and radically affecting agriculture with a move towards smart systems. The outcome of this transition is improved efficiency, reduced environmental pollution, and enhanced productivity of crops.
Nondestructive Evaluation of Agro-products by Intelligent Sensing Techniques is a reference which provides readers timely updates in the progress of intelligent sensing techniques used for nondestructive evaluation of agro-products. Chapters, each contributed by experts in food safety and technology, describe existing and innovative techniques that could be or have been applied to agro-products quality and safety evaluation, processing, harvest, traceability, and so on. The book includes 11 individual chapters, with each chapter focusing on a specific aspect of intelligent sensing techniques applied in agriculture. Specifically, the first chapter introduces the reader to representative techniques and methods for nondestructive evaluation. Subsequent chapters present detailed information about the processing and quality evaluation of agro-products (e.g., fruits, and vegetables), food grading, food tracing, and the use of robots for harvesting specialty crops.
- - 11 chapters, contributed by experts that cover basic and applied research in agriculture
- introduces readers to nondestructive evaluation techniques
- covers food quality evaluation processes
- covers food grading and traceability systems
- covers frontier topics that represent future trends (robots and UAVs used in agriculture)
- familiarizes the readers with several intelligent sensing technologies used in the agricultural sector (including machine vision, near-infrared spectroscopy, hyperspectral/multispectral imaging, bio-sensing, multi-technology fusion detection)
- provides bibliographic references for further reading
- gives applied examples on both common and specialty crops
This reference is intended as a source of updated information for consultants, students and academicians involved in agriculture, crops science and food biotechnology. Professionals involved in food safety and security planning and policymaking will also benefit from the information presented by the authors.
About the Authors
Dr. Jiangbo Li received his Ph.D. degree from the College of Biosystems Engineering and Food Science from Zhejiang University. Dr. Li is a researcher at the Research Center of Intelligent Equipment for Agriculture of Beijing Academy of Agriculture and Forestry Sciences. His current areas of research include optical sensing, such as near-infrared spectroscopy, hyperspectral and multispectral imaging techniques and computer vision, all emphasize on solving practical problems in agricultural engineering. Especially, his innovative studies on fruit quality inspection by optical sensing techniques have been widely reported. The results of his work have been published in more than 80 peer-reviewed journal papers.
Dr. Zhao Zhang is a research assistant professor in the Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND, USA. His major research direction relates to sensing and automation in agriculture, focusing on the application and development of innovative technologies (e.g., UAVs and ground vehicle-based sensors) to support sustainable agriculture. Before joining his current position, Dr. Zhang worked as a postdoc research associate with USDA-ARS Sugarbeet and Bean research unit, which is located in Lansing, MI, USA. During his postdoc, Dr. Zhang developed an apple harvest and infield sorting system that helped him win the 2019 ASABE Rain Bird Engineering Concept. Dr. Zhang earned his Ph.D. degree from Pennsylvania State University, PA, USA, and his Ph.D. project was to develop and optimize an apple harvest-assist unit.
Key words:agro-products, food safety, food biotechnology, bio-sensing, nondestructive evaluation techniques, crops science, agriculture, Artificial Intelligence
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