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Artificial Intelligence in Agriculture is a groundbreaking hardback book authored by Rajesh Singh, Anita Gehlot, Mahesh Kumar Prajapat, and Bhupendra Singh. This comprehensive resource is tailored for individuals looking to explore the transformative potential of Artificial Intelligence (AI) in the agricultural sector. The book adopts a practical, hands-on approach to demystifying core AI concepts, including machine learning, deep learning, computer vision, and expert systems, supplemented with real-world examples for enhanced comprehension. With an introduction to Python programming, this resource is especially suitable for readers who may lack prior programming experience. The content is meticulously organized into two parts: the first part lays the foundational knowledge of AI in agriculture, detailing its various branches and applications. The second part delves into the implementation of algorithms, guiding readers through the application of various AI libraries to develop insightful and practical projects. By the end of the book, readers will not only grasp the essential branches of AI applicable to agriculture but also acquire practical skills to tackle real-world agricultural issues, such as crop health prediction, field surveillance, and plant species recognition. This book is an essential resource for farmers, agricultural technologists, researchers, and anyone interested in the intersection of AI and agriculture.
Key Features
| Features | Description |
|---|---|
| Comprehensive Learning Resource | Offers foundational to advanced understanding of AI in agriculture. |
| Hands-On Approach | Includes practical examples and projects. |
| Python Programming Introduction | Accessible for readers with no prior programming experience. |
| Two Parts Structure | First part covers fundamentals; second part focuses on implementation. |
| Real-World Applications | Covers crop health prediction, field surveillance, and species recognition. |
| Expert Contributions | Authored by experts in agriculture and AI. |
| Algorithm and Library Implementation | Guides readers on using various AI libraries effectively. |
| Attributes | Description |
|---|---|
| Title | Artificial Intelligence in Agriculture |
| Authors | Rajesh Singh, Anita Gehlot, Mahesh Kumar Prajapat, Bhupendra Singh |
| Format | Hardback |
| Topics Covered | Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Python Programming |
| Target Audience | Farmers, agricultural specialists, researchers, and students |
| Publication Year | 2023 |
| Publisher | Not Specified |
*Disclaimer: This above description has been AI generated and has not been audited or verified for accuracy. It is recommended to verify product details independently before making any purchasing decisions.
Brand: NIPA
Country of Origin: India
"This book is a comprehensive resource for individuals who wish to explore the potential of Artificial Intelligence (AI) in agriculture, either for the purpose of gaining a foundational understanding or expanding their existing knowledge. The book provides readers with a practical, hands-on approach to learning about AI, machine learning, deep learning, computer vision, and expert systems, with real-world examples to aid comprehension.
Furthermore, the book includes an introduction to the basics of Python programming, making it accessible to readers with little or no prior programming experience. The book is divided into two parts: the first part covers the fundamentals of AI in agriculture, including its various branches and their applications. The second part focuses on the implementation of algorithms and the use of different machine learning, deep learning, and computer vision libraries to develop practical and insightful projects.
"This book is a comprehensive resource for individuals who wish to explore the potential of Artificial Intelligence (AI) in agriculture, either for the purpose of gaining a foundational understanding or expanding their existing knowledge. The book provides readers with a practical, hands-on approach to learning about AI, machine learning, deep learning, computer vision, and expert systems, with real-world examples to aid comprehension.
Furthermore, the book includes an introduction to the basics of Python programming, making it accessible to readers with little or no prior programming experience. The book is divided into two parts: the first part covers the fundamentals of AI in agriculture, including its various branches and their applications. The second part focuses on the implementation of algorithms and the use of different machine learning, deep learning, and computer vision libraries to develop practical and insightful projects.
Upon completion of this book, readers will have gained an understanding of the various branches of AI and their applicable scenarios, as well as the standard workflow for approaching and solving machine-learning problems. Additionally, readers will be equipped with the skills necessary to tackle real-world problems in the agriculture sector, such as crop health prediction, field surveillance analytics, and the recognition of plant species. With this knowledge, readers will be well-prepared to leverage the power of AI in the agriculture field.
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Content
"1. Artificial Intelligence 2. Learning Python for Artificial Intelligence 3. Machine Learning 4. Deep Learning 5. Computer Vision 6. Knowledge Based Expert System 7. Tools for Artificial Intelligence 8. Important Libraries for AI 9. Machine Learning Algorithms 10. Disease Classification and Detection in Plants 11. Species Recognition in Flowers 12. Precision Farming
"
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