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.

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