• Nayan Paliwal
• Atharva Thorat
• Saurav Naik

Butterflies are one of the most vibrant and diverse species, making them a crucial subject of study in various fields such as biology, conservation, and environmental science. Their classification based on visual features is an essential task for ecological monitoring and biodiversity research, as the diversity and distribution of butterfly species can serve as indicators of environmental health. Manual classification, however, is not only labor-intensive but also prone to errors, particularly when large datasets are involved or when species exhibit subtle visual differences. With advancements in artificial intelligence, particularly deep learning, automated image classification systems have gained significant traction. Convolutional Neural Networks (CNNs) have emerged as powerful tools for image recognition, excelling in tasks requiring the analysis of complex patterns, textures, and colors. In this research, we explore the task of butterfly image classification using multiple machine learning techniques, including CNNs, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The dataset comprises images of various butterfly species, and the goal is to accurately classify them based on their visual features. We employ several data augmentation techniques to make the model more robust to variations in image quality and orientation. Furthermore, the models are evaluated based on accuracy scores, confusion matrices, and other performance metrics such as precision, recall, and F1-score. The study highlights the superior performance of CNNs in complex image classification tasks and discusses potential improvements, such as incorporating transfer learning, to enhance model accuracy and generalizability in future research.

Butterfly classification, Convolutional Neural Networks (CNN), Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Data Augmentation, Deep Learning, Image Recognition, Performance Evaluation.

"Butterfly Image Classification: A Comparative Analysis of CNN and Traditional Machine Learning Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.e77-e87, October-2024, Available :http://www.jetir.org/papers/JETIR2410409.pdf

"Butterfly Image Classification: A Comparative Analysis of CNN and Traditional Machine Learning Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. ppe77-e87, October-2024, Available at : http://www.jetir.org/papers/JETIR2410409.pdf