In comparison to the competitors, engineers must provide quick, low-cost, and dependable solutions. The advancement of
intelligence generated by machines and its application in almost every field has created a need to reduce the human role in image
processing while also making time and labor profit. Lepidopterology is the discipline of entomology dedicated to the scientific
analysis of caterpillars and the three butterfly superfamilies. Students studying lepidopterology must generally capture
butterflies with nets and dissect them to discover the insect’s family types and shape. This research work aims to assist science
students in correctly recognizing butterflies without harming the insects during their analysis. This paper discusses transferlearning-based neural network models to identify butterfly species. The datasets are collected from the Kaggle website, which
contains 10,035 images of 75 different species of butterflies. From the available dataset, 15 unusual species were selected,
including various butterfly orientations, photography angles, butterfly lengths, occlusion, and backdrop complexity. When we
analyzed the dataset, we found an imbalanced class distribution among the 15 identified classes, leading to overfitting. The
proposed system performs data augmentation to prevent data scarcity and reduce overfitting. The augmented dataset is also
used to improve the accuracy of the data models. This research work utilizes transfer learning based on various convolutional
neural network architectures such as VGG16, VGG19, MobileNet, Xception, ResNet50, and InceptionV3 to classify the butterfly
species into various categories. All the proposed models are evaluated using precision, recall, F-Measure, and accuracy. The
investigation findings reveal that the InceptionV3 architecture provides an accuracy of 94.66%, superior to all other architectures.
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