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Smart Big Data in Digital Agriculture Applications

Acquisition, Advanced Analytics, and Plant Physiology-informed Artificial Intelligence

  • Book
  • © 2024

Overview

Authors:
  1. Haoyu Niu

    1. Texas A&M Institute of Data Science (TAMIDS), Texas A&M University, College Station, USA

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    You can also search for this author in PubMed   Google Scholar

  2. YangQuan Chen

    1. Department of Mechanical Engineering, University of California, Merced, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Summarizes the state-of-the-art agriculture applications with small UAV
  • Highlights new field methods for data gathering with machine learning
  • Special focuses on smart data acquisition and analysis

Part of the book series: Agriculture Automation and Control (AGAUCO)

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Table of contents (14 chapters)

  1. Front Matter

    Pages i-xviii
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  2. Why Big Data Is Not Smart Yet?

    1. Front Matter

      Pages 1-1
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    2. Introduction

      • Haoyu Niu, YangQuan Chen
      Pages 3-14

    3. Why Do Big Data and Machine Learning Entail the Fractional Dynamics?

      • Haoyu Niu, YangQuan Chen
      Pages 15-53

  3. Smart Big Data Acquisition Platforms

    1. Front Matter

      Pages 55-55
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    2. Small Unmanned Aerial Vehicles (UAVs) and Remote Sensing Payloads

      • Haoyu Niu, YangQuan Chen
      Pages 57-81

    3. The Edge-AI Sensors and Internet of Living Things (IoLT)

      • Haoyu Niu, YangQuan Chen
      Pages 83-97

    4. The Unmanned Ground Vehicles (UGVs) for Digital Agriculture

      • Haoyu Niu, YangQuan Chen
      Pages 99-109

  4. Advanced Big Data Analytics, Plant Physiology-Informed Machine Learning, and Fractional-Order Thinking

    1. Front Matter

      Pages 111-111
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    2. Fundamentals of Big Data, Machine Learning, and Computer Vision Workflow

      • Haoyu Niu, YangQuan Chen
      Pages 113-128

    3. A Low-Cost Proximate Sensing Method for Early Detection of Nematodes in Walnut Using Machine Learning Algorithms

      • Haoyu Niu, YangQuan Chen
      Pages 129-147

    4. Tree-Level Evapotranspiration Estimation of Pomegranate Trees Using Lysimeter and UAV Multispectral Imagery

      • Haoyu Niu, YangQuan Chen
      Pages 149-163

    5. Individual Tree-Level Water Status Inference Using High-Resolution UAV Thermal Imagery and Complexity-Informed Machine Learning

      • Haoyu Niu, YangQuan Chen
      Pages 165-179

    6. Scale-Aware Pomegranate Yield Prediction Using UAV Imagery and Machine Learning

      • Haoyu Niu, YangQuan Chen
      Pages 181-195

  6. Back Matter

    Pages 235-239
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Keywords

About this book

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.



Authors and Affiliations

  • Texas A&M Institute of Data Science (TAMIDS), Texas A&M University, College Station, USA

    Haoyu Niu

  • Department of Mechanical Engineering, University of California, Merced, USA

    YangQuan Chen

About the authors

Haoyu Niu is a research engineer in the Institute of Data Science at Texas A&M University, College Station (TAMIDS). He works at the intersection of data science and cyberinfrastructure for smart agriculture. He joins a team of researchers from TAMIDS, Texas A&M’s Department of Electrical & Computer Engineering, AgriLife Research, AgriLife Extension, and the School of Performance, Visualization and Fine Arts on a project to develop data-driven infrastructure that enables efficient irrigation using AI-derived crop-growth model based in UAV imaging and environmental data. His research interests include machine learning, computer vision, and robotics. More specifically, he is interested in applying big data, deep learning, and remote sensing technology for data analysis. He received his Ph.D. from Electrical Engineering and Computer Science department at UC, Merced.

YangQuan Chen is a full professor at the School of Engineering, University of California,Merced. He was on the faculty of Electrical and Computer Engineering Dept. of Utah State University, Logan, Utah from 2000 to 2012 where he was a tenured associate professor and the Director for the Center for Self-Organizing and Intelligent Systems (CSOIS) from 2004-2012. Dr. Chen published many books, papers and patents. He served as the General Co-Chair for ICUAS 2014 and 2017, 2019 (International Conference on Unmanned Aircraft Systems), and on the editorial board for journals like IEEE Trans. on Control Systems Technology, ISA Trans., ASME Journal of Dynamics Systems, Measurement and Control, IFAC journals of Control Engineering Practice as well as IFAC Mechatronics, Fractional Calculus and Applied Analysis, and Nonlinear Dynamics. He also serves as the Topical Editor-in-Chief (Field Robotics), International Journal of Advanced Robotics Systems (IJARS) and Intelligent Service Robotics. He serves as an editorial board member of MDPI Applied Sciences, Sensors. His ISI Publon H-index is 67 with more than 19.6k citations, GoogleScholar citation is 43.2k with H-index 91, H10-index 540. Dr. Chen was in the 2018, 2019, 2010 and 2021 Highly Cited Researchers List by Clarivate. He won Research of The Year awards from Utah State University (2012) and UC Merced (2020) respectively. In 2018, for his cumulative work in drones, Dr. Chen won "Senate Distinguished Scholarly Public Service award" that recognizes a faculty member who has energetically and creatively applied his or her professional expertise and scholarship to benefit the local, regional, national or international community.

Bibliographic Information

  • Book Title: Smart Big Data in Digital Agriculture Applications

  • Book Subtitle: Acquisition, Advanced Analytics, and Plant Physiology-informed Artificial Intelligence

  • Authors: Haoyu Niu, YangQuan Chen

  • Series Title: Agriculture Automation and Control

  • DOI: https://doi.org/10.1007/978-3-031-52645-9

  • Publisher: Springer Cham

  • eBook Packages: Biomedical and Life Sciences, Biomedical and Life Sciences (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024

  • Hardcover ISBN: 978-3-031-52644-2Published: 29 February 2024

  • Softcover ISBN: 978-3-031-52647-3Due: 31 March 2024

  • eBook ISBN: 978-3-031-52645-9Published: 28 February 2024

  • Series ISSN: 2731-3492

  • Series E-ISSN: 2731-3506

  • Edition Number: 1

  • Number of Pages: XVIII, 239

  • Number of Illustrations: 1 b/w illustrations

  • Topics: Agriculture, Plant Physiology, Statistics, general, Engineering Design

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