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Digital Ecosystem for Innovation in Agriculture

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Cover of 'Digital Ecosystem for Innovation in Agriculture'

Table of Contents

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    Book Overview
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    Chapter 1 A Brief Review of Tools to Promote Transdisciplinary Collaboration for Addressing Climate Change Challenges in Agriculture by Model Coupling
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    Chapter 2 Machine Learning and Deep Learning in Crop Management—A Review
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    Chapter 3 Need for an Orchestration Platform to Unlock the Potential of Remote Sensing Data for Agriculture
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    Chapter 4 An Algorithmic Framework for Fusing Images from Satellites, Unmanned Aerial Vehicles (UAV), and Farm Internet of Things (IoT) Sensors
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    Chapter 5 Globally Scalable and Locally Adaptable Solutions for Agriculture
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    Chapter 6 A Theoretical Framework of Agricultural Knowledge Management Process in the Indian Agriculture Context
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    Chapter 7 Simple and Innovative Methods to Estimate Gross Primary Production and Transpiration of Crops: A Review
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    Chapter 8 Role of Virtual Plants in Digital Agriculture
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    Chapter 9 Remote Sensing for Mango and Rubber Mapping and Characterization for Carbon Stock Estimation—Case Study of Malihabad Tehsil (UP) and West Tripura District, India
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    Chapter 10 Impact of Vegetation Indices on Wheat Yield Prediction Using Spatio-Temporal Modeling
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    Chapter 11 Farm-Wise Estimation of Crop Water Requirement of Major Crops Using Deep Learning Architecture
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    Chapter 12 Hyperspectral Remote Sensing for Agriculture Land Use and Land Cover Classification
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    Chapter 13 Computer Vision Approaches for Plant Phenotypic Parameter Determination
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Title
Digital Ecosystem for Innovation in Agriculture
Published by
Springer Nature Singapore, June 2023
DOI 10.1007/978-981-99-0577-5
ISBNs
978-9-81-990576-8, 978-9-81-990577-5
Editors

Chaudhary, Sanjay, Biradar, Chandrashekhar M., Divakaran, Srikrishnan, Raval, Mehul S.

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