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IoT and AI in Agriculture

Self- sufficiency in Food Production to Achieve Society 5.0 and SDG's Globally

  • Book
  • © 2023

Overview

Editors:
  1. Tofael Ahamed

    1. Faculty of Life & Environmental Sciences, University of Tsukuba, Tsukuba, Japan

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  • Covers key technologies and techniques such as IoT, AI, and ML in the development of smart agriculture
  • Provides information for different types of hardware, platforms, and machine learning techniques used in smart farming
  • Presents examples and exercises in each chapter

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Table of contents (23 chapters)

  1. Front Matter

    Pages i-xvii
    Download chapter PDF

  2. IoT × AI: Introducing Agricultural Innovation for Global Food Production

    • Munirah Hayati Hamidon, Mohammad Hussain Seyar, P. D. Kahandage, Victor Massaki Nakaguchi, Arkar Minn, Ailian Jiang et al.
    Pages 1-20

  3. Strategic Short Note: Transforming Controlled Environment Plant Production Toward Circular Bioeconomy Systems

    • K. C. Ting
    Pages 21-24

  4. Artificial Lighting Systems for Plant Growth and Development in Indoor Farming

    • Munirah Hayati Hamidon, Tofael Ahamed
    Pages 25-46

  5. An IoT-Based Precision Irrigation System to Optimize Plant Water Requirements for Indoor and Outdoor Farming Systems

    • Mohammad Hussain Seyar, P. D. Kahandage, Tofael Ahamed
    Pages 47-69

  6. Strategic Short Note: Artificial Intelligence and Internet of Things: Application in Urban Water Management

    • Martin Anda, Roberta Fornarell, Stewart Dallas
    Pages 71-74

  7. Purification of Agricultural Polluted Water Using Solar Distillation and Hot Water Producing with Continuous Monitoring Based on IoT

    • P. D. Kahandage, Mohammad Hussain Seyar, Ryozo Noguchi, Tofael Ahamed
    Pages 75-96

  8. Long Range Wide Area Network (LoRaWAN) for Oil Palm Soil Monitoring

    • Yee Nie Goh, Diyana Jamaludin, Hazreen Haizi Harith, Alfadhl Yahya Alkhaled, Nurul Adilah Abdul Latiff, Samsuzana Abd Aziz
    Pages 97-124

  9. Strategic Short Note: Application of Smart Machine Vision in Agriculture, Forestry, Fishery, and Animal Husbandry

    • Kai-Rong Chang, Tsung-Hsiang Ma, Yan-Fu Kuo
    Pages 125-131

  10. Artificial Intelligence in Agriculture: Commitment to Establish Society 5.0: An Analytical Concepts Mapping for Deep Learning Application

    • Victor Massaki Nakaguchi, Tofael Ahamed
    Pages 133-152

  11. Potentials of Deep Learning Frameworks for Tree Trunk Detection in Orchard to Enable Autonomous Navigation System

    • Ailian Jiang, Ryozo Noguchi, Tofael Ahamed
    Pages 153-178

  12. Real-Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT

    • Addie Ira Borja Parico, Tofael Ahamed
    Pages 179-218

  13. Pear Recognition System in an Orchard from 3D Stereo Camera Datasets Using Deep Learning Algorithms

    • Siyu Pan, Tofael Ahamed
    Pages 219-252

  14. Thermal Imaging and Deep Learning Object Detection Algorithms for Early Embryo Detection: A Methodology Development Addressed to Quail Precision Hatching

    • Victor Massaki Nakaguchi, Tofael Ahamed
    Pages 253-281

  15. Strategic Short Note: Intelligent Sensing and Robotic Picking of Kiwifruit in Orchard

    • Longsheng Fu
    Pages 283-288

  16. Low-Cost Automatic Machinery Development to Increase Timeliness and Efficiency of Operation for Small-Scale Farmers to Achieve SDGs

    • Arkar Minn, Tofael Ahamed
    Pages 289-309

  17. Vision-Based Leader Vehicle Trajectory Tracking for Multiple Agricultural Vehicles

    • Linhuan Zhang, Tofael Ahamed, Yan Zhang, Pengbo Gao, Tomohiro Takigawa
    Pages 311-333

  18. Autonomous Robots in Orchard Management: Present Status and Future Trends

    • R. M. Rasika D. Abeyrathna, Tofael Ahamed
    Pages 335-366

  19. Strategic Short Note: Comparing Soil Moisture Retrieval from Water Cloud Model and Neural Network Using PALSAR-2 for Oil Palm Estates

    • Veena Shashikant, Abdul Rashid Mohamed Shariff, Aimrun Wayayok, Md Rowshon Kamal, Yang Ping Lee, Wataru Takeuchi
    Pages 367-371

  20. Development of a Recognition System for Spraying Areas from Unmanned Aerial Vehicles Using a Machine Learning Approach

    • Pengbo Gao, Yan Zhang, Linhuan Zhang, Ryozo Noguchi, Tofael Ahamed
    Pages 373-393
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Keywords

About this book

This book reviews recent innovations in the smart agriculture space that use the Internet of Things (IoT) and sensing to deliver Artificial Intelligence (AI) solutionsto agricultural productivity in the agricultural production hubs. In this regard, South and Southeast Asia are one of the major agricultural hubs of the world, facing challenges of climate change and feeding the fast-growing population. To address such challenges, a transboundary approach along with AI and BIG data for bioinformatics are required to increase yield and minimize pre- and post-harvest losses in intangible climates to drive the sustainable development goal (SDG) for feeding a major part of the 9 billion population by 2050 (Society 5.0 SDG 1 & 2). Therefore, this book focuses on the solution through smart IoT and AI-based agriculture including pest infestation and minimizing agricultural inputs for in-house and fields production such as light, water, fertilizer and pesticides to ensure food security aligns with environmental sustainability. It provides a sound understanding for creating new knowledge in line with comprehensive research and education orientation on how the deployment of tiny sensors, AI/Machine Learning (ML), controlled UAVs, and IoT setups for sensing, tracking, collection, processing, and storing information over cloud platforms for nurturing and driving the pace of smart agriculture in this current time. 


The book will appeal to several audiences and the contents are designed for researchers, graduates, and undergraduate students working in any area of machine learning, deep learning in agricultural engineering, smart agriculture, and environmental science disciplines. Utmost care has been taken to present a varied range of resource areas along with immense insights into the impact and scope of IoT, AI and ML in the growth of intelligent digital farming and smart agriculture which will give comprehensive information to the targeted readers. 


Editors and Affiliations

  • Faculty of Life & Environmental Sciences, University of Tsukuba, Tsukuba, Japan

    Tofael Ahamed

About the editor

Tofael Ahamed is an Associate Professor, at the Faculty of Life and Environmental Sciences, the University of Tsukuba, which is one of the leading universities in Japan. Dr. Ahamed performs research in the field of precision agriculture technology, agricultural robotics, and decision support systems. He focuses on enabling smart applicationsusing the Internet of Things (IoT) and Artificial Intelligence (AI) in agriculture, where crop production varies spatially and temporally within the field boundaries depending on the soil and environmental conditions. Dr. Ahamed is also member of the American Society of Agricultural and Biological Engineers, Japanese Society of Agricultural Machinery, Food Engineers, Japanese Society of Agricultural Information, and Japan Section of Regional Science Association. He is also serving as one of the Associate Editors for Computer and Electronics in Agriculture, Agricultural Information Research, Editorial Member for Asia-Pacific Journal of Regional Science, Author and Editor of serval books and Guest Editor of special issues for Remote Sensing. 

Bibliographic Information

  • Book Title: IoT and AI in Agriculture

  • Book Subtitle: Self- sufficiency in Food Production to Achieve Society 5.0 and SDG's Globally

  • Editors: Tofael Ahamed

  • DOI: https://doi.org/10.1007/978-981-19-8113-5

  • Publisher: Springer Singapore

  • 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 Singapore Pte Ltd. 2023

  • Hardcover ISBN: 978-981-19-8112-8Published: 11 April 2023

  • Softcover ISBN: 978-981-19-8115-9Published: 12 April 2024

  • eBook ISBN: 978-981-19-8113-5Published: 10 April 2023

  • Edition Number: 1

  • Number of Pages: XVII, 461

  • Number of Illustrations: 1 b/w illustrations

  • Topics: Agriculture, Machine Learning, Sustainable Development

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