Data Science in Agriculture and Natural Resource Management

by G. P. Obi Reddy, J. Adinarayana, Mehul S. Raval, Sanjay Chaudhary

Length: 334 pages
Edition: 1
Language: English
Publisher: Springer
Publication Date: 2021-11-12
ISBN-10: 9811658463
ISBN-13: 9789811658464
Sales Rank: #0 (See Top 100 Books)

This book aims to address emerging challenges in the field of agriculture and natural resource management using the principles and applications of data science (DS). The book is organized in three sections, and it has fourteen chapters dealing with specialized areas. The chapters are written by experts sharing their experiences very lucidly through case studies, suitable illustrations and tables. The contents have been designed to fulfil the needs of geospatial, data science, agricultural, natural resources and environmental sciences of traditional universities, agricultural universities, technological universities, research institutes and academic colleges worldwide. It will help the planners, policymakers and extension scientists in planning and sustainable management of agriculture and natural resources. The authors believe that with its uniqueness the book is one of the important efforts in the contemporary cyber-physical systems.

Cover
Front Matter
Part I. Data Science—Principles, Concepts and Applications
    Data Science—Algorithms and Applications in Earth Observation
    Emerging Technologies—Principles and Applications in Precision Agriculture
    Data Science: Principles and Concepts in Modeling Decision Trees
    Deep Reinforcement Learning for Agriculture: Principles and Use Cases
Part II. Data Science Applications in Agriculture
    Computer Vision and Machine Learning in Agriculture
    An Architecture for Quality Centric Crop Production System
    Crop Classification for Precision Farming Using Machine Learning Algorithms and Sentinel-2 Data
    Machine Learning Approaches and Sentinel-2 Data in Crop Type Mapping
    Big Data Analytics for Climate-Resilient Food Supply Chains: Opportunities and Way Forward
Part III. Data Science Applications in Natural Resource Management
    Machine Learning Algorithms for Optical Remote Sensing Data Classification and Analysis
    Geo-Big Data in Digital Augmentation and Accelerating Sustainable Agroecosystems
    Transforming Soil Paradigms with Machine Learning
    Remote Sensing and Machine Learning for Identification of Salt-affected Soils
    Geoportal Platforms for Sustainable Management of Natural Resources
Back Matter