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Application of Machine Learning in Agriculture
Explores the possibility of market, tools and technologies for smart agriculture with the focus on machine learning
Mohammad Ayoub Khan (Edited by), Rijwan Khan (Edited by), Mohammad Aslam Ansari (Edited by)
9780323905503, Elsevier Science
Paperback / softback, published 19 May 2022
330 pages, 220 illustrations (50 in full color)
23.5 x 19 x 2.1 cm, 0.45 kg
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.
Part 1: Fundamentals of Smart Agriculture
1. Machine learning based Agriculture
2. Monitoring agriculture essentials
3. Livestock management in agriculture
Part 2: Market, Technology and products
4. Agriculture Economics
5. Digital Marketing and its impact
6. Technology and products
Part 3: Tools and Techniques
7. Modeling Techniques used in Smart Agriculture
8. Diseases detection
9. Food Security
10. Medicines Care Management
11. Detection and diagnosis of plant diseases
12. Machine Learning Technique for agriculture image recognition
Subject Areas: Enterprise software [UFL], Information technology: general issues [UB], Agricultural engineering & machinery [TVD], Technology: general issues [TB], Agriculture & related industries [KNAC]
