This book is a comprehensive resource that handles the issues of sustainable agriculture and natural resource management, aligned with the United Nations' Sustainable Development Goals (SDGs). The book is organized into five sections, Understanding the Problem, Data Collection and Cleaning, Exploratory Data Analysis and Visualization, Model Building, and Model Deployment. Each section covers a critical aspect of data science in this context and addresses specific SDGs 2zero hunger, 6clean water and sanitation, 12responsible consumption and production, 13climate action, and 15Life on land. The organized sections are arranged to seamlessly follow the data science pipeline and provide practical guidance from problem understanding to its model deployment and stakeholder management. The book is useful for researchers, students, practitioners, and policymakers.

Discusses issues of sustainable agriculture and natural resource management Covers a critical aspect of data science for five Sustainable Development Goals Serves as a reference for researchers, students, practitioners, and policymakers

Autorentext

Mehul S Raval, Ph.D., is Associate Dean and Professor at the School of Engineering and Applied Science, Ahmedabad University, India. Earlier, he served PDPU Gandhinagar, DA-IICT Gandhinagar, and SCET Surat. He was Visiting Faculty at SVNIT Surat and VNSG University Surat. His academic pursuits involve visits to the Graduate School of Natural Science and Technology, Okayama University, Japan, Argosy Visiting Associate Professor at Olin College of Engineering, MA, the US (2016), and Sacred Heart University, the US (2019). He is Alumnus of the Electronics & Telecommunication Engineering Department, College of Engineering Pune (COEP), and the University of Pune. He has published in journals, magazines, conferences, and workshops on the national and international stage. He is Technical Program Committee Member leading national and international conferences, workshops, and symposiums. He reviews IEEE, ACM, Springer, Elsevier, IET, and other leading publishers.

Sanjay Chaudhary, Ph.D., is Professor and Associate Dean at the School of Engineering and Applied Science, Ahmedabad University. He was Dean of Students of Ahmedabad University from 2020 to 2022. From 2001 to 2013, he was Professor and Dean of Academic Programs) at Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT), Gandhinagar, India. His research areas are Cloud Computing, Blockchain Technology, Data Analytics, and ICT Applications in Agriculture and Rural Development. He has authored nine books and eleven book chapters. He has published over a hundred research papers in international conferences, workshops, and journals. He is an Active Member of program committees of leading International conferences and seminars and review committees of leading journals. He has received research grants from leading organizations, including IBM, Microsoft, and the Department of Science and Technology, Govt. of India.

J. Adinarayana, Ph.D., has been Teaching/Research Faculty Member since 1986 and is an Adjunct Professor at the Centre of Studies in Resources Engineering (CSRE), IIT Bombay, India. His areas of expertise include agro-informatics in contemporary agriculture. As a Team Leader, he led various interdisciplinary national and international R&D projects on digital agriculture. He served as President of the APFITA (Asia-Pacific Federation for Information Technology in Agriculture (2018-2022) as well as INSAIT (Indian Society of Agrl. Information Technology) (2018-2024). He received the JSPS Invitation Fellowship, DST Young Scientist Project, and INSA Visiting Scientist/Faculty awards. He is also Expert Member of various committees on IT in Agriculture formed by the Government of India. He has published more than 100 research papers in top international journals and supervised 13 PhD and more than 35 MTech students in the field of Agro-Informatics. and has published about 100 papers in tier-1 journals. He is a strong believer of application of inter-disciplinary approach to the contemporary challenges in agriculture systems.

Wei Guo, Ph.D., is Associate Professor and PI at the Laboratory of Field Phenomics, Graduate School of Agricultural and Life Sciences, the University of Tokyo, Japan. Trained as Engineer in computer science/informatics in China and Japan, he received his Ph.D. in agriculture, majoring in agro-informatics at the University of Tokyo in 2014. His research focuses on field-based phenotyping using advanced sensing platforms and technologies such as drones and ground robots, image processing, and machine learning approaches. In 2020, he received the Young Researcher's Award from the Japanese Society of Agricultural Informatics. He has published over sixty journal papers in the field of plant phenomics.

Inhalt

Introduction to Data Science in Agriculture and Natural Resource Management.- Defining Problems and Identifying Opportunities in Agriculture and Natural Resources.- Preprocessing of Agricultural and Natural Resource Data.- A Robust big data handling solution for RGB image data set by indoor UAV based phenotyping system.- Mapping Aboveground Biomass and Soil Organic Carbon Density in India- A geospatial-analytic framework for Integrating multi-year remote sensing, large field surveys, and machine learning.- Statistical Modeling in Agriculture: From Foundational Concepts to Modern Applications.- EasyIDP v2.0: An Intermediate Data Processing Package for Photogrammetry-Based Plant Phenotyping.- Deep Learning: A Catalyst for Sustainable Agriculture Transformation.- Deep Learning and Reinforcement Learning Methods for Advancing Sustainable Agricultural and Natural Resource Management.- A Review on AI and Remote Sensing-Based Regenerative Agriculture Assessment.- Model Evaluation and Selection: Ensuring Robust and Accurate Predictions of Crop Yields in Agriculture.- Evaluation of hybrid biodegradable sensor node for monitoring soil moisture.- Multi-modal AI for Ultra-precision Agriculture.- Future Perspectives: Emerging Technologies and Ethical Considerations in Data Science for Agriculture and Natural Resources.