Application of Machine Learning in Agriculture
- 1st Edition - May 14, 2022
- Imprint: Academic Press
- Editors: Mohammad Ayoub Khan, Rijwan Khan, Mohammad Aslam Ansari
- Language: English
- 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 econo… Read more
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Request a sales quoteApplication 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.
- Addresses the technology of smart agriculture from a technical perspective
- Reveals opportunities for technology to improve and enhance not only yield and quality, but the economic value of a food crop
- Discusses physical instruments, simulations, sensors, and markets for machine learning in agriculture
Scientists and academicians involved in the research and development (R & D) of agriculture, botany, chemistry, nanotechnology and biotechnology for knowledge base and future research. Further, this book would help the agro-industrialists for the R & D on carbon dots based plant growth enhancer
- 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
- Edition: 1
- Published: May 14, 2022
- Imprint: Academic Press
- No. of pages: 330
- Language: English
- Paperback ISBN: 9780323905503
- eBook ISBN: 9780323906685
MK
Mohammad Ayoub Khan
Mohammad Ayoub Khan is Associate Professor at the University of Bisha in Bisha, Saudi Arabia.
Affiliations and expertise
Associate Professor, University of Bisha, Bisha, Saudi ArabiaRK
Rijwan Khan
Rijwan Khan is Professor and Head of Department of Computer Science at the ABES Institute of Technology in Uttar Pradesh, India.
Affiliations and expertise
Professor and Head of Department of Computer Science, ABES Institute of Technology, Uttar Pradesh, IndiaMA
Mohammad Aslam Ansari
Scientist Professor, Department of Agriculture Communication, College of Agriculture, G B Pant University of Agriculture and Technology, Pantnagar (Uttarakhand). Post-doctoral studies at University of Leicester (UK). Received Senior Research Fellowship during doctoral studies and UNDP/ICAR scholarship during post-graduation at Pantnagar University. He is chairman of the editorial board of Indian Journal of Science Communication and has nearly 25 years’ experience in different capacities in corporate, governmental, NGO and Academic institutions. He has worked in that time has included focuse on the use of technologies in agricultural settings, including the use of mobile phones, e-learning, and internet use. His research interests include ICT applications in agriculture and rural development, Development Communication, Training, Health Communication and Science Communication.
Affiliations and expertise
Scientist Professor, Department of Agriculture Communication, College of Agriculture, G B Pant University of Agriculture and Technology, Pantnagar (Uttarakhand), IndiaRead Application of Machine Learning in Agriculture on ScienceDirect