Internet of things and machine learning in agriculture : technological impacts and challenges. / edited by Vishal Jain [and three others].

Format:

Book

Contributor:

Jain, Vishal, 1983- editor.

Series:

De Gruyter frontiers in computational intelligence ; Volume 8.

De Gruyter frontiers in computational intelligence ; Volume 8

Language:

English

Subjects (All):

Agriculture--Data processing.

Agriculture.

Physical Description:

1 online resource (426 pages).

Place of Publication:

Berlin : De Gruyter, [2021]

Summary:

Agriculture is one of the most fundamental human activities. As the farming capacity has expanded, the usage of resources such as land, fertilizer, and water has grown exponentially, and environmental pressures from modern farming techniques have stressed natural landscapes. Still, by some estimates, worldwide food production needs to increase to keep up with global food demand. Machine Learning and the Internet of Things can play a promising role in the Agricultural industry, and help to increase food production while respecting the environment. This book explains how these technologies can be applied, offering many case studies developed in the research world.

Contents:

Frontmatter

Preface

Acknowledgments

Contents

List of contributors

Part I: Machine learning and Internet of things in agriculture

1 Smart farming: using IoT and machine learning techniques

2 Food security and farming through IoT and machine learning

3 An innovative combination for new agritechnological era

4 Recent advancements and challenges of artificial intelligence and IoT in agriculture

5 Technological impacts and challenges of advanced technologies in agriculture

Part II: Applications of Internet of things in agriculture

6 IoT-based platform for smart farming - Kaa

7 Internet of things platform for smart farming

8 Internet of things platform for smart farming

9 Internet of things platform for smart farming

Part III: Applications of machine learning in agriculture

10 Kisan-e-Mitra: a tool for soil quality analyzer and recommender system

11 Artificial intelligence for plant disease detection: past, present, and future

12 Wheat rust disease identification using deep learning

13 Image-based hibiscus plant disease detection using deep learning

14 Rainfall prediction by applying machine learning technique

15 Plant leaf disease classification based on feature selection and deep neural network

16 Using deep learning for image-based plant disease detection

17 Using deep learning for image-based plant disease detection

18 Using deep learning for image-based plant disease detection

Index

Notes:

Description based on print version record.

Includes index.

ISBN:

3-11-069127-2