AI and ML Techniques

Introduction to AI and ML in Animal Behavior

  • Overview of the application of AI and ML in the field of animal behavior studies.

  • Presented by Aftab Siddique, PhD, College of Agricultural Science, Fort Valley State University.

Introduction to AI & ML

  • Artificial Intelligence (AI): A branch of computer science dedicated to creating machines that think, learn, and make decisions similarly to humans.

  • Machine Learning (ML): A subset of AI focusing on enabling computers to learn from data and examples rather than explicit programming.

  • Example: Teaching a computer to recognize dogs by exposing it to many dog images.

Big Data Analytics

  • Definition: The process of analyzing large and diverse datasets using advanced analytical techniques.

  • The 4 V's of Data:

    • Value of Data: Importance and utility of the data analyzed.

    • Velocity of Data Generation: Speed at which data is produced.

    • Volume of Data: Quantity of data collected and analyzed.

    • Variability of Generated Data: Fluctuations in data types and structures over time.

Scale of Big Data

  • Daily Data Generation:

    • 333.2 billion emails sent.

    • 4.5 billion likes on Facebook.

    • 1.7 trillion photos uploaded in 2022.

    • Approximately 14 billion devices connected to the internet in 2022.

    • 35 billion RFID tags utilized for data collection in the previous year.

Types of Analytics

  • Descriptive Analytics: What is happening?

  • Diagnostic Analytics: Why did it happen?

  • Predictive Analytics: What is likely to happen?

  • Prescriptive Analytics: What should I do about it?

  • Illustrates the inter-connection and flow of different types of analytics in studying and predicting behaviors.

Understanding Animal Behavior

  • Definition: The actions and interactions of animals with their environment.

  • Importance of studying animal behavior for understanding survival, communication, and reproduction.

  • Practical applications: Protecting wildlife, maintaining pet health, enhancing farming methods.

  • Example: Understanding why pets engage in certain behaviors or migratory patterns in animals.

AI in Animal Behavior Studies

  • Role of AI and ML: Efficiently processing massive data amounts to study animal behavior effectively.

  • Example: AI analyzes video footage of animals, like lions, to understand hunting patterns much faster than traditional observation methods.

  • Common techniques include computer vision and pattern recognition for identifying behaviors.

Tracking Animals with Technology

  • Computer Vision and RFID-GPS Systems: Utilize technology to identify and monitor animal movements in difficult environments.

  • Example: Tracking small ruminants in Africa to inform on their territories and prevent human-animal conflicts.

Analyzing Animal Sounds with AI

  • Bioacoustics: The study of animal sounds to understand communication using AI.

  • Application: Identifying unique whale songs to track and comprehend whale behaviors.

  • Fun Fact: Whale songs can be heard for miles underwater.

Animal Emotions and Facial Recognition

  • Detecting Emotions: AI can recognize facial expressions in animals, such as dogs and primates, to assess their emotional states.

  • Example: Analyzing subtle facial changes in dogs to determine if they are stressed or calm, aiding veterinarians and pet owners.

  • Visual aids can illustrate various animal expressions.

Predicting Animal Behavior with ML

  • ML Models: Can predict animal behavior based on historical data and environmental conditions.

  • Example: Predicting animal hunting patterns influenced by weather to aid in conservation efforts.

  • Important for protecting endangered species through avoidance strategies.

AI in Agriculture: Monitoring Farm Animals

  • Monitoring Livestock: AI usage in observing animal health and movements.

  • Example: Detecting limping in cows or early signs of illness in chickens to improve animal welfare and farm productivity.

Importance of AI and ML in Animal Studies

  • Efficiency: AI saves researchers time and resources, enabling studies in remote or inaccessible areas using drones and cameras.

  • Enhanced Understanding: Offers insights into behaviors that human observers may miss, contributing to conservation and improving animal welfare.

  • Conservation Efforts: Vital for protecting endangered species and reducing human-wildlife conflicts.

Conclusion

  • Thanks and invitation for questions.