Machine Learning-Based Classification of Termite Genera

This paper presents an approach for the automatic identification of termite species based on the measurements of various body parts and the use of a random forest classifier model. Termites are known for causing significant economic damage worldwide, particularly in tropical regions. Accurate identification of termite species is essential for effective management and control. This study presents an AI-driven approach for the automatic classification of termite genera. Utilizing Random Forest and other machine learning algorithms, our method achieves a classification accuracy of 99%, significantly enhancing the efficiency of entomological research. The model's robustness was validated against a comprehensive dataset, demonstrating its potential in automated pest identification and management. The research provides a detailed methodology, critical analysis of the results, and discusses the implications of using AI in biological classification.