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Application of Machine Learning in Agriculture

1st Edition - May 14, 2022

Editors: Mohammad Ayoub Khan, Rijwan Khan, Mohammad Aslam Ansari

Paperback ISBN:

9 7 8 - 0 - 3 2 3 - 9 0 5 5 0 - 3

eBook ISBN:

9 7 8 - 0 - 3 2 3 - 9 0 6 6 8 - 5

Application of Machine Learning in Smart Agriculture is the first book to present a multidisciplinary look at how technology can not only improve agricultural output, but the… Read more

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Application of Machine Learning in Smart Agriculture is the first book to present a multidisciplinary look at how technology can not only improve agricultural output, but the economic efficiency of that output as well. Through a global lens, the book approaches the subject from a technical perspective, providing important knowledge and insights for effective and efficient implementation and utilization of machine learning.

As artificial intelligence techniques are being used to increase yield through optimal planting, fertilizing, irrigation, and harvesting, these are only part of the complex picture which must also take into account the economic investment and its optimized return. The performance of machine learning models improves over time as the various mathematical and statistical models are proven. Presented in three parts, Application of Machine Learning in Smart Agriculture looks at the fundamentals of smart agriculture; the economics of the technology in the agricultural marketplace; and a diverse representation of the tools and techniques currently available, and in development.

This book is an important resource for advanced level students and professionals working with artificial intelligence, internet of things, technology and agricultural economics.

Cover image
Title page
Table of Contents
Copyright
List of contributors
Section 1: Fundamentals of smart agriculture
Chapter 1. Machine learning-based agriculture
Abstract
Introduction
Literature review
Deep learning in agriculture
Proposed method
Comparative study
Results and discussions
Conclusion
References
Chapter 2. Monitoring agricultural essentials
Abstract
Introduction
Unsupervised machine learning algorithms for agriculture
Supervised machine learning algorithms for agriculture
Proposed predictive model for agriculture
Results and discussion
Summary
References
Chapter 3. Machine learning-based remote monitoring and predictive analytics system for monitoring and livestock monitoring
Abstract
Introduction
Motivation
Background study
Reported work
Comparative analysis
Conclusion
References
Section 2: Market, technology and products
Chapter 4. Agricultural economics
Abstract
Introduction
Prediction of crop price
Impact of gross domestic product
Economical changes in traditional agriculture versus machine learning agriculture
Meteorology
Conclusion
References
Chapter 5. Current and prospective impacts of digital marketing on the small agricultural stakeholders in the developing countries
Abstract
Introduction
Definition of and types of electronic business
Digital agricultural market before, during, and what is expected after the COVID-19 pandemic in developing countries
Digital agricultural market to mitigate the negative impacts of uncertainty
Opportunities and risks of investment in the digital agricultural market industry
Market segmentation of the digital agricultural market in developing countries
A mobile banking system
Digital agricultural value chain and its stakeholders
Impacts of digital agriculture on poverty reduction, food security rates, and food losses and waste reduction in developing countries
Agricultural digitalization to achieve the sustainable development goals 2030
Conclusion
References
Chapter 6. Intelligent farming system through weather forecast support and crop production
Abstract
Introduction
Technology stack used
Used algorithms
System-related architecture
Weather prediction
Methodology used
Results
Conclusions
References
Chapter 7. Deep learning-based prediction for stand age and land utilization of rubber plantation
Abstract
Introduction
Background and related work
Study materials
Solution design and implementation
Model evaluation
Discussion
Conclusion
Acknowledgment
References
Section 3: Tools and techniques
Chapter 8. Modeling techniques used in smart agriculture
Abstract
Introduction
Expert system
Fuzzy framework for smart agriculture
Conclusion
References
Chapter 9. Plant diseases detection using artificial intelligence
Abstract
Introduction
Literature survey
Recognizing plant diseases
Image acquisition
Image preprocessing
Image segmentation
Feature extraction
Image recognition
Performance measures for image recognition techniques
Discussion and future work
Conclusion
References
Chapter 10. A deep learning-based approach for mushroom diseases classification
Abstract
Introduction
Related works
Dataset description
Methods
Result analysis and discussion
Conclusion
References
Chapter 11. Smart fence to protect farmland from stray animals
Abstract
Introduction
Smart fence to protect farmland
Virtual fence setup using optical fiber sensor
Optical fiber cable as sensor
Types of fiber-optic sensor systems
Classification of fiber-optic sensors on the basis of operating principles
Signal analysis
Algorithm for classification
Results
Conclusion
References
Chapter 12. Enhancing crop productivity through autoencoder-based disease detection and context-aware remedy recommendation system
Abstract
Introduction
Preliminaries
Proposed method
Experimental valuation
Conclusion
References
Chapter 13. UrbanAgro: Utilizing advanced deep learning to support Sri Lankan urban farmers to detect and control common diseases in tomato plants
Abstract
Introduction
Literature review
Implementation
Results and discussion
Conclusion
Acknowledgments
References
Chapter 14. Machine learning techniques for agricultural image recognition
Abstract
Introduction
Steps for image analysis
Machine learning strategies in agricultural image recognition
Applications of image processing in agriculture tasks
Summary
References
Index