Data science for agricultural innovation and productivity / edited by S. Gowrishankar [and four others].

Format:

Book

Author/Creator:

Gowrishankar, S, Author.

Contributor:

S, Gowrishankar, editor.

Language:

English

Subjects (All):

Sustainable agriculture.

Physical Description:

1 online resource (229 pages)

Edition:

First edition.

Place of Publication:

Singapore : Bentham Science Publishers Ltd., [2024]

Summary:

Data Science for Agricultural Innovation and Productivity explores the transformation of agriculture through data-driven practices. This comprehensive book delves into the intersection of data science and farming, offering insights into the potential of big data analytics, machine learning, and IoT integration. Readers will find a wide range of topics covered in 10 chapters, including smart farming, AI applications, hydroponics, and robotics. Expert contributors, including researchers, practitioners, and academics in the fields of data science and agriculture, share their knowledge to provide readers with up-to-date insights and practical applications. The interdisciplinary emphasis of the book gives a well-rounded view of the subject. With real-world examples and case studies, this book demonstrates how data science is being successfully applied in agriculture, inspiring readers to explore new possibilities and contribute to the ongoing transformation of the agricultural sector. Sustainability and future outlook are the key themes, as the book explores how data science can promote environmentally conscious agricultural practices while addressing global food security concerns. Key Features: Focus on data-driven agricultural practices Comprehensive coverage of modern farming topics with an interdisciplinary perspective ‍ Expert insights Sustainability and future outlook Highlights practical applications Data Science for Agricultural Innovation and Productivity is an essential resource for researchers, data scientists, farmers, agricultural technologists, students, educators, and anyone with an interest in the future of farming through data-driven agriculture. Readership Researchers, data scientists, farmers, agricultural technologists, students, educators, and general readers.

Contents:

Cover

Title

Copyright

End User License Agreement

Contents

Foreword

Preface

List of Contributors

Digital Twin for Smart Farming

Galiveeti Poornima1,*, Deepak S. Sakkari1 and Sukruth Gowda M.A.1

INTRODUCTION

TECHNOLOGIES USED IN SMART FARMING

DEFINITION OF DIGITAL TWIN

DIGITAL TWIN TYPOLOGY

DIGITAL TWINS IN FARM MANAGEMENT

APPLICATION OF DIGITAL TWINS IN SMART FARMING

USE CASES

CONCLUSION

FUTURE SCOPE

REFERENCES

Deep Learning Models for Prediction of Disease in Lycopersicum

Nakatha Arun Kumar1,* and Sathish S. Kumar2

RELATED WORK

Pretrained Models

VGGNet

GoogleNet

ResNet

Inception

Methodology

RESULTS

CONCLUDING REMARKS

A Smart Hydroponics System for Sustainable Agriculture

Supriya Jaiswal1,*, Gopal Rawat1, Chetan Khadse1 and Sohit Sharma1

TYPES OF FARMING

Hydroponic Farming

Scope and Challenges

ADVANTAGES AND LIMITATIONS OF HYDROPONICS

GOVERNMENT INITIATIVES AND RECENT RESEARCH TECHNOLOGIES TO SUPPORT FARMING

COMPONENTS AND STRUCTURE OF SMART HYDROPONIC FARMING

Supply System for Automated Water Pumping

Sensor Network for Smart Farming

Internet of Hydroponics (IoH): Architecture and Working

MACHINE LEARNING AND DATA MINING IN HYDROPONICS

Applications of Machine Learning

Challenges Faced in Machine Learning

Use Cases of ML in Hydroponics

Future Area of Research in Hydroponics

Agriculture Robotics

Bogala Mallikharjuna Reddy1,*

AUTOMATION OF FARMING

PRECISION AGRICULTURE ROBOTICS

IOT-BASED SMART AGRICULTURE

ROBOTICS IN AGRICULTURE

Classification of Robots

Agriculture Robotics Evolution

Cooperative Agricultural Robotics

APPLICATIONS OF AGRICULTURE ROBOTICS

Farmland Preparation.

Sowing and Planting

Inspection

Spraying and Plant Treatment

Yield and Phenotype Estimating

Harvesting

Commercial Agricultural Robots

CHALLENGES IN AGRICULTURE ROBOTICS

CONCLUSION AND FUTURE OUTLOOK

Internet of Green Things (IoGT) for Carbon-Free Economy

Sadiq Mohammed Sanusi1,*, Singh Invinder Paul2 and Ahmad Muhammad Makarfi3

MAJOR IMPACTS OF CLIMATE CHANGE ON HEALTH

THE LOW CARBON ECONOMY AND ICT

GLOBAL RISKS 2019 REPORT: FAILURE OF CLIMATE CHANGE MITIGATION AND ADAPTATION

INTERNET OF HEALTH THINGS (IOHT)

IOT APPLICATION IN COMBATING CLIMATE CHANGE

Agriculture

Stopping Illegal Logging and Deforestation

Smart Cities

Utilities

Waste Control

Transportation and Traffic

Data About the Climate and the Environment

Automated Buildings and Energy Storage

IoT-Based Environment Solutions Examples

IOT ENVIRONMENTAL TECHNOLOGY PROJECT CASE STUDY BY ERICSSON

INDUSTRIAL INTERNET OF THINGS (IIOTS) IS THE FUTURE

Challenges of Industrial Internet of Things (IIoT)

Energy Savings

Performance in Real-Time

Interoperability and Coexistence

Privacy and Security

IOT FUTURE PERSPECTIVES FOR CARBON-FREE ECOLOGY

Revolutionizing Precision Agriculture Using Artificial Intelligence and Machine Learning

Jayalakshmi Murugan1,*, Maharajan Kaliyanandi1 and Carmel Sobia M.2

Untitled

BACKGROUND

LITERATURE SURVEY

DATA SETS

FEATURE EXTRACTION FOR DISEASE IDENTIFICATION

PERFORMANCE COMPARISON

CONCLUSION AND FUTURE WORK

Internet of Fisheries Things (IOFT) for Blue Economy &amp

Ecosystem

DIGITALIZATION OF AQUACULTURE

Definition of Digitization in the Fisheries Sector.

Digitalization Use in the Aquaculture Sector

Location Determination Using GIS

Making Use of Technology for Automatic Feeders

Automatic Evaluation of Water Quality

Marketing Aquaculture Products Online

IOT FOR MONITORING SHRIMP/FISH POND

APPS FOR FISHERIES AND AQUACULTURE USE OF IOT IN MOBILE

Aquaculture-Related Mobile Applications

Marine Fisheries-Related Mobile Apps

Mobile Apps for Marketing

FOOD SUPPLY CHAIN MANAGEMENT IN THE AGE OF DIGITALIZATION

Technologies that can be Employed in the Context of the IoT for the Supply Chain

Tea Rhizospheres and Their Functional Role in Tea Gardens

Rwitabrata Mallick1,*

METHODOLOGY

Withering

Rolling

Fermentation

Drying

Rhizosphere

DISCUSSION

Study Area Under Kurseong subdivision

Applications of Smart Farming Sensors: A Way Forward

Prasenjit Pal1,* and Sandeep Poddar2

SMART FARMING: AN EMERGING CONCEPT

DIFFERENT SENSORS USED IN AGRICULTURE

Optical Sensors:

Electrochemical Sensors:

Dielectric Sensors:

Location Sensors in Agriculture:

Electronic Sensors

Airflow Sensors

Sensors used in Agriculture

WHAT ARE THE BENEFITS OF SENSORS IN AGRICULTURE?

Excelled Efficiency

Expansion

Reduced Sources

Cleaning Procedure

Agility

Improved Production and Quality

Monitoring Weather Situations

Greenhouse Automation

Crop Tracking

Drones

APPLICATIONS OF SENSORS IN FARMING

Applications in Animal and dairy science:

CHALLENGES TO ADOPT SENSOR BASED APPLICATIONS

An Overview of Building a Global Data Area on the Web for Farming

R. Sapna1,*, Ravva Akash Guptha2, Paritala Venkateswara Rao2 and Raavi Sai Pranay2

ABOUT THE WEB AND ITS HISTORY.

First Era (Web 1.0)

Second Era (Web 2.0)

Third Era (WEB 3.0)

SEMANTIC WEB STACK

Semantic Web Technologies

Hypertext Web Technologies

Standardized Semantic Web Technologies

Unrealized Semantic Web Technologies

Machine Learning on Semantic Web

Semantic Web and Agriculture

The Semantic Web Technology for Agriculture

The Semantic Resources for Agriculture

Subject Index

Back Cover.

Notes:

Includes bibliographical references.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9789815196177

9815196170

OCLC:

1424748456