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Smart Big Data in Digital Agriculture Applications: Acquisition, Advanced Analytics, and Plant Physiology-informed Artificial Intelligence (Agriculture Automation and Control)

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Book Details
Language
English
Publishers
Springer; 2024th edition (29 Feb. 2024)
Weight
0.54 KG
Publication Date
02/04/2024
ISBN-10
3031526449
Pages
257 pages
ISBN-13
9783031526442
Dimensions
15.6 x 1.6 x 23.39 cm
SKU
9783031526442
Author Name
Haoyu Niu (Author)
Biosketch: YangQuan Chen (SM'95-SrM'98) received the B.S. degree in industrial automation from the University of Science and Technology of Beijing, Beijing, China, in 1985, the M.S. degree in automatic control from the Beijing Institute of Technology, Beijing, in 1989, and the Ph.D. degree in advanced control and instrumentation from the Nanyang Technological University, Singapore, Singapore, in 1998. He had been an Associate Professor of electrical and computer engineering at Utah State University, Logan, and the Director of the Center for Self-Organizing and Intelligent Systems. Since 2012, he joined University of California, Merced, the first research university established in the 21st century in USA where he started MESA Lab (mechatronics, embedded systems and applications http://mechatronics.ucmerced.edu). He is the holder of 13 U.S. patents in various aspects of hard disk drive servomechanics and one US patent on fractional order controller tuning. He has published over 200 journal papers, over 20 book chapter papers, over 300 refereed conference papers, and more than 50 industrial technical reports. His current areas of research interests include: distributed measurement and distributed control of distributed parameter systems using mobile actuator and sensor networks, smart mechatronics and process controls, applied fractional calculus in controls, signal processing and energy informatics, multi-UAV based cooperative personal remote sensing and real time water management and irrigation control. Dr. Chen is an Associate Editor on the Conference Editorial Board of the Control Systems Society of the IEEE (since 2002) and an Associate Editor on the Instrument Society of the America Editorial Board for the American Control Conference (since 2004). He was the General Chair for IEEE/ASME Int. Conf. on Mechatronics and Embedded Systems Applications (MESA) 2010, Qingdao, China and served as Program Chair for the ASME/IEEE Int. Conf. on MESA, Las Vegas, NV, in 2007 and MESA09 San Diego, CA, 2009 and Program Co-Chair for the IEEE International Conference on Mechatronics and Automation for 2006 and 2007. He is the TC Chair for MESA under ASME DED, Chair for MES for IEEE ITSS, and a member of IFAC TC2.2. Dr. Chen is a member of AMA, AWRA, AUVSI, ASME, IEEE, and the American Society for Engineering Education. He serves as an Associate Editor for IET Control Theory and Applications, ISA Transactions, IFAC journals Mechatronics and Control Engineering Practice (CEP), Acta Montanistica Slovaca, Fractional Calculus and Applied Analysis (FCAA), Fractional Differential Calculus (FDC), ASME J. of Dynamic Systems, Measurement and Control and IEEE Transactions on Control Systems Technology (TCST). He is a member of Editorial Advisory Board of An International Journal of Optimization and Control: Theories & Applications (IJOCTA); a founding editorial board member of Unmanned Systems (2013-2015). Currently, Dr. Chen serves as the TC Co-Chair for IEEE Robotics and Automation Society on Aerial Robotics and Unmanned Aerial Vehicles, Program Co-Chair for ICUAS (Int. Conf. on Unmanned Aricraft Systems) 2016. Since 2015, he serves on IEEE-USA's Committee on Transportation and Aerospace Policy (CTAP) representing IEEE RAS. He is a member of IFAC TC 2.2. His ISI H-index is 35 and Google Scholar MyCitation H-index is 58 and the i10-index is 308.Webpage is at http://mechatronics.ucmerced.edu/GoogleMyCitation: http://scholar.google.com/citations?user=RDEIRbcAAAAJ&hl=en http://www.researcherid.com/rid/A-2301-2008https://www.researchgate.net/profile/YangQuan_ChenRead more about this authorRead less about this author
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In the dynamic realm of digital agriculture, the integration of big data acquisition platforms has sparked both curiosity and enthusiasm among researchers and agricultural practitioners. This book embarks on a journey to explore the intersection of artificial intelligence and agriculture, focusing on small-unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), edge-AI sensors and the profound impact they have on digital agriculture, particularly in the context of heterogeneous crops, such as walnuts, pomegranates, cotton, etc.

For example, lightweight sensors mounted on UAVs, including multispectral and thermal infrared cameras, serve as invaluable tools for capturing high-resolution images. Their enhanced temporal and spatial resolutions, coupled with cost effectiveness and near-real-time data acquisition, position UAVs as an optimal platform for mapping and monitoring crop variability in vast expanses.

This combination of data acquisition platforms and advanced analytics generates substantial datasets, necessitating a deep understanding of fractional-order thinking, which is imperative due to the inherent “complexity” and consequent variability within the agricultural process. Much optimism is vested in the field of artificial intelligence, such as machine learning (ML) and computer vision (CV), where the efficient utilization of big data to make it “smart” is of paramount importance in agricultural research.

Central to this learning process lies the intricate relationship between plant physiology and optimization methods. The key to the learning process is the plant physiology and optimization method.

Crafting an efficient optimization method raises three pivotal questions: 1. ) What represents the best approach to optimization? 2.

) How can we achieve a more optimal optimization? 3. ) Is it possible to demand “more optimal machine learning,” exemplified by deep learning, while minimizing the need for extensive labeled data for digital agriculture? This book details the foundations of the plant physiology-informed machine learning (PPIML) and the principle of tail matching (POTM) framework.

It is the 9th title of the "Agriculture Automation and Control" book series published by Springer. .

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