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Harnessing data science for sustainable agriculture and natural resource management

Record Type:	Electronic resources : Monograph/item
Title/Author:	Harnessing data science for sustainable agriculture and natural resource managementedited by Mehul S. Raval ... [et al.].
other author:	Raval, Mehul S.
Published:	Singapore :Springer Nature Singapore :2024.
Description:	xxi, 355 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Sustainable agricultureData processing.
Online resource:	https://doi.org/10.1007/978-981-97-7762-4
ISBN:	9789819777624$q(electronic bk.)

Harnessing data science for sustainable agriculture and natural resource management
Harnessing data science for sustainable agriculture and natural resource management[electronic resource] /edited by Mehul S. Raval ... [et al.]. - Singapore :Springer Nature Singapore :2024. - xxi, 355 p. :ill. (chiefly color), digital ;24 cm. - Studies in big data,v. 1612197-6511 ;. - Studies in big data ;v.1..

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.

ISBN: 9789819777624$q(electronic bk.)

Standard No.: 10.1007/978-981-97-7762-4doiSubjects--Topical Terms:

986903
Sustainable agriculture
--Data processing.

LC Class. No.: S494.5.S86

Dewey Class. No.: 338.10285

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520 $a 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.
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