04836nam a22005775i 4500 978-981-97-7762-4 DE-He213 20251030114628.0 cr nn 008mamaa 241219s2024 si | s |||| 0|eng d 9789819777624 978-981-97-7762-4 10.1007/978-981-97-7762-4 doi Q342 UYQ bicssc COM004000 bisacsh UYQ thema Harnessing Data Science for Sustainable Agriculture and Natural Resource Management [electronic resource] / edited by Mehul S. Raval, Sanjay Chaudhary, J. Adinarayana, Wei Guo. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XXI, 355 p. 131 illus., 113 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Studies in Big Data, 2197-6511 ; 161 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. 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 2—zero hunger, 6—clean water and sanitation, 12—responsible consumption and production, 13—climate action, and 15—Life 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. Computational intelligence. 64052 Artificial intelligence Data processing. Sustainability. 57661 Computational Intelligence. 64052 Data Science. Sustainability. 57661 Raval, Mehul S. editor. (orcid)0000-0002-3895-1448 https://orcid.org/0000-0002-3895-1448 edt http://id.loc.gov/vocabulary/relators/edt 89213 Chaudhary, Sanjay. editor. (orcid)0000-0001-9127-360X https://orcid.org/0000-0001-9127-360X edt http://id.loc.gov/vocabulary/relators/edt 89214 Adinarayana, J. editor. (orcid)0000-0002-8753-9919 https://orcid.org/0000-0002-8753-9919 edt http://id.loc.gov/vocabulary/relators/edt 89215 Guo, Wei. editor. (orcid)0000-0002-3017-5464 https://orcid.org/0000-0002-3017-5464 edt http://id.loc.gov/vocabulary/relators/edt 70447 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819777617 Printed edition: 9789819777631 Printed edition: 9789819777648 Studies in Big Data, 2197-6511 ; 161 68724 https://doi.org/10.1007/978-981-97-7762-4 ZDB-2-INR ZDB-2-SXIT 65863 65863