Applying Artificial Intelligence, Machine Learning, and Prompt Engineering for Image Recognition of Burmese Pythons

AUTHOR(S):

Rishi Iyer, Alpa Raval, Abhijit S. Pandya

TITLE

Applying Artificial Intelligence, Machine Learning, and Prompt Engineering for Image Recognition of Burmese Pythons

ABSTRACT

Google's Teachable Machine, Meta’s Segment Anything Model, and the Python programming language are utilized to develop a machine-learning model for identifying Burmese pythons and alligators in natural settings in the Everglades using image recognition. Aimed at enhancing wildlife conservation efforts, the model was trained using over 1000 image samples. It demonstrates potential for automated wildlife monitoring, with promising initial results in species identification, classification, and geographic distribution, especially for invasive species like the Burmese Python in extensive remote wetlands like the Everglades. Prompt engineering lets us reuse powerful models for wildlife detection without retraining, which makes AI faster and easier to use. This approach underscores the value of accessible Artificial Intelligence tools in environmental management and species protection.

KEYWORDS

prompt engineering, artificial intelligence, machine learning, wildlife conservation

Cite this paper

Rishi Iyer, Alpa Raval, Abhijit S. Pandya. (2025) Applying Artificial Intelligence, Machine Learning, and Prompt Engineering for Image Recognition of Burmese Pythons. International Journal of Computers, 10, 220-225