Internet of Things and Machine Learning in Agriculture: Technological Impacts and Challenges

✍ Scribed by Jyotir Moy Chatterjee (editor); Abhishek Kumar (editor); Pramod Singh Rathore (editor); Vishal Jain (editor)

• Publisher: De Gruyter
• Year: 2021
• Tongue: English
• Leaves: 426
• Series: De Gruyter Frontiers in Computational Intelligence; 8
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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