Lepidoptera Classification Using Convolutional Neural Network EfficientNet-B0

Abstract

Butterflies and moths are insects that have many different species. Butterflies and moths have considerable aesthetic, ecosystem, health, economic, health, and scientific values. However, because there are so many different varieties and patterns, it is vital to divide them by type for better identification. By creating a Convolutional Neural Network (CNN) algorithm that produces accurate results, a deep learning approach can be used to classify the types of butterfly and moth species. This paper offer an Lepidoptera including butterfly and moth classification model based on convolutional neural networks.  3390 images of 25 different butterfly and moth species were acquired with various images orientations, angles, distance, and background.   Using the EfficientNet-B0 CNN architecture, different types of butterflies and moths are classified and input into the EfficientNet-B0 model. EfficientNet-B0 performs feature extraction on the image, so that it can be used to perform classification and then combined through a pooling process and connected to the final layer to produce a classification probability. The probability indicates how likely the image is to belong to a particular type or class of butterfly or moth.  In comparison to earlier studies, the test results indicate an improvement in butterfly and moth classification. Increased accuracy was seen with values of 97.91% accuracy, 97% recall,  97% precision, and 97% F1-Score. This paper novelty is the enhancement of the CNN architecture EfficientNet-B0 used in image classification, which results in improved image classification accuracy.