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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xiii 7. Section 1: Fundamentals of smart agriculture - p. xvi 8. Chapter 1. Machine learning-based agriculture - p. 3 9. Chapter 2. Monitoring agricultural essentials - p. 29 10. Chapter 3. Machine learning-based remote monitoring and predictive analytics system for monitoring and livestock monitoring - p. 47 11. Section 2: Market, technology and products - p. 63 12. Chapter 4. Agricultural economics - p. 71 13. Chapter 5. Current and prospective impacts of digital marketing on the small agricultural stakeholders in the developing countries - p. 91 14. Chapter 6. Intelligent farming system through weather forecast support and crop production - p. 113 15. Chapter 7. Deep learning-based prediction for stand age and land utilization of rubber plantation - p. 131 16. Section 3: Tools and techniques - p. 153 17. Chapter 8. Modeling techniques used in smart agriculture - p. 159 18. Chapter 9. Plant diseases detection using artificial intelligence - p. 173 19. Chapter 10. A deep learning-based approach for mushroom diseases classification - p. 191 20. Chapter 11. Smart fence to protect farmland from stray animals - p. 213 21. Chapter 12. Enhancing crop productivity through autoencoder-based disease detection and context-aware remedy recommendation system - p. 239 22. Chapter 13. UrbanAgro: Utilizing advanced deep learning to support Sri Lankan urban farmers to detect and control common diseases in tomato plants - p. 263 23. Chapter 14. Machine learning techniques for agricultural image recognition - p. 283 24. Index - p. 307
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xiii 7. Section 1: Fundamentals of smart agriculture - p. xvi 8. Chapter 1. Machine learning-based agriculture - p. 3 9. Chapter 2. Monitoring agricultural essentials - p. 29 10. Chapter 3. Machine learning-based remote monitoring and predictive analytics system for monitoring and livestock monitoring - p. 47 11. Section 2: Market, technology and products - p. 63 12. Chapter 4. Agricultural economics - p. 71 13. Chapter 5. Current and prospective impacts of digital marketing on the small agricultural stakeholders in the developing countries - p. 91 14. Chapter 6. Intelligent farming system through weather forecast support and crop production - p. 113 15. Chapter 7. Deep learning-based prediction for stand age and land utilization of rubber plantation - p. 131 16. Section 3: Tools and techniques - p. 153 17. Chapter 8. Modeling techniques used in smart agriculture - p. 159 18. Chapter 9. Plant diseases detection using artificial intelligence - p. 173 19. Chapter 10. A deep learning-based approach for mushroom diseases classification - p. 191 20. Chapter 11. Smart fence to protect farmland from stray animals - p. 213 21. Chapter 12. Enhancing crop productivity through autoencoder-based disease detection and context-aware remedy recommendation system - p. 239 22. Chapter 13. UrbanAgro: Utilizing advanced deep learning to support Sri Lankan urban farmers to detect and control common diseases in tomato plants - p. 263 23. Chapter 14. Machine learning techniques for agricultural image recognition - p. 283 24. Index - p. 307
