 Название: Machine Learning in Farm Animal Behavior using Python
Автор: Natasa Kleanthous, Abir Hussain
Издательство: CRC Press
Год: 2025
Страниц: 412
Язык: английский
Формат: pdf (true), epub
Размер: 28.5 MB
This book is a comprehensive guide to applying Machine Learning to animal behavior analysis, focusing on activity recognition in farm animals. It begins by introducing key concepts of animal behavior and ethology, followed by an exploration of Machine Learning techniques, including supervised, unsupervised, semi-supervised, and reinforcement learning. The practical section covers essential steps like data collection, preprocessing, exploratory data analysis, feature extraction, model training, and evaluation, using Python.
The book emphasizes the importance of high-quality data and discusses various sensors and annotation methods for effective data collection. It addresses key Machine Learning challenges such as generalization and data issues. Advanced topics include feature selection, model selection, hyperparameter tuning, and Deep Learning algorithms. Practical examples and Python implementations are provided throughout, offering hands-on experience for researchers, students, and professionals aiming to apply Machine Learning to animal behavior analysis.
Prerequisites:
Before proceeding into the contents of the book, readers are expected to have:
• Python Programming Knowledge: A foundational understanding of Python programming is essential. While the book is accompanied by an online introduction to Python basics, familiarity with its syntax and standard libraries will be advantageous.
• Basic Machine Learning Concepts: An understanding of core Machine Learning concepts will be beneficial; however, this book provides foundational insights intended for individuals who are not yet familiar with the particulars of Machine Learning ideas.
• Interest in Animal Behavior and Agriculture: Though not a technical requirement, a genuine interest in animal behavior and the application of technology in agriculture will enhance the learning experience.
Who is This Book For?
This book is designed for:
• Students studying animal science, agriculture, or Computer Science with a curiosity in the convergence of these fields.
• Researchers & academics focused on animal behavior studies wanting to incorporate data-driven methodologies.
• Data scientists & developers wishing to gain new insights for application areas related to animal activity recognition in the agriculture domain.
• Agriculturalists & farmers who need to stay informed about the power of Machine Learning for insights into animal behavior, health, and productivity.
Contents:
Preface. 1. Introduction to Machine Learning for Farm Animal Behavior 2. Machine Learning Concepts and Challenges. 3. A Practical Example to Building a Simple Machine Learning Model 4. Sensors, Data Collection, and Annotation 5. Preprocessing and Feature Extraction for Animal Behavior Research 6. Feature Selection Techniques 7. Animal Research: Supervised and Unsupervised Learning Algorithms 8. Evaluation, Model Selection and Hyperparameter Tuning 9. Deep Learning Algorithms for Animal Activity Recognition References
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