Description

Book Synopsis
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.



Table of Contents
Data Science: Principles and Concepts in Data Analysis and Modelling.- Data Science: Tools, Techniques and Potential Applications in Earth Observation Studies.- Data Science in Agriculture and Natural Resource Management: An Overview.- Applications of Reinforcement Learning and Recurrent Neural Network Based Deep Learning Frameworks in Agriculture.- Precision Farming Using Emerging Technologies.- An Architecture for Quality Centric Crop Production.- Integrating UAV and Field Sensor Data for Better Decision Making in Broadacre Cropping Systems.- Object Based Crop Classification for Precision Farming.- Disruptive Innovations in Precision Agriculture - Towards BD Analytics for Better GeoFarmatics.- A Paradigm-shift in Global Cropland Maps and Products for Food and Water Security in the Twenty-first Century: Petabyte Scale Satellite Big-data Analytics, Machine Learning, and Cloud Computing.- Big Data Analytics for Climate Resilient Supply Chains: Opportunities and Way Forward.- Mapping Croplands Using Machine Learning Algorithms and Spectral Matching Techniques.- Applications of Computer Vision in Precision Agriculture.- Innovative Geoportal Platforms for Sustainable Management of Natural Resources.

Data Science in Agriculture and Natural Resource

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£125.99

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RRP £139.99 – you save £14.00 (10%)

Order before 4pm today for delivery by Thu 22 Jan 2026.

A Hardback by G. P. Obi Reddy, Mehul S. Raval, J. Adinarayana

1 in stock


    View other formats and editions of Data Science in Agriculture and Natural Resource by G. P. Obi Reddy

    Publisher: Springer Verlag, Singapore
    Publication Date: 12/10/2021
    ISBN13: 9789811658464, 978-9811658464
    ISBN10: 9811658463

    Description

    Book Synopsis
    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.



    Table of Contents
    Data Science: Principles and Concepts in Data Analysis and Modelling.- Data Science: Tools, Techniques and Potential Applications in Earth Observation Studies.- Data Science in Agriculture and Natural Resource Management: An Overview.- Applications of Reinforcement Learning and Recurrent Neural Network Based Deep Learning Frameworks in Agriculture.- Precision Farming Using Emerging Technologies.- An Architecture for Quality Centric Crop Production.- Integrating UAV and Field Sensor Data for Better Decision Making in Broadacre Cropping Systems.- Object Based Crop Classification for Precision Farming.- Disruptive Innovations in Precision Agriculture - Towards BD Analytics for Better GeoFarmatics.- A Paradigm-shift in Global Cropland Maps and Products for Food and Water Security in the Twenty-first Century: Petabyte Scale Satellite Big-data Analytics, Machine Learning, and Cloud Computing.- Big Data Analytics for Climate Resilient Supply Chains: Opportunities and Way Forward.- Mapping Croplands Using Machine Learning Algorithms and Spectral Matching Techniques.- Applications of Computer Vision in Precision Agriculture.- Innovative Geoportal Platforms for Sustainable Management of Natural Resources.

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