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Digital Ecosystem for Innovation in Agriculture

Overview of attention for book
Cover of 'Digital Ecosystem for Innovation in Agriculture'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    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
Attention for Chapter 5: Globally Scalable and Locally Adaptable Solutions for Agriculture
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Chapter title
Globally Scalable and Locally Adaptable Solutions for Agriculture
Chapter number 5
Book title
Digital Ecosystem for Innovation in Agriculture
Published by
Springer, Singapore, January 2023
DOI 10.1007/978-981-99-0577-5_5
Book ISBNs
978-9-81-990576-8, 978-9-81-990577-5
Authors

Pranuthi, Gogumalla, Srikanth, Rupavatharam

Timeline

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 1 11%
Unspecified 1 11%
Student > Ph. D. Student 1 11%
Lecturer 1 11%
Student > Master 1 11%
Other 0 0%
Unknown 4 44%
Readers by discipline Count As %
Unspecified 1 11%
Environmental Science 1 11%
Nursing and Health Professions 1 11%
Business, Management and Accounting 1 11%
Engineering 1 11%
Other 0 0%
Unknown 4 44%