From Pixels to Taxonomy: Advanced Insect Species Classification with EfficientNetB3 Transfer Learning

Insects are a varied and plentiful collection of organisms that fulfil vital functions in ecosystems across the world. The identification and classification of insect species are crucial for comprehending their ecological importance and the influence of environmental changes on their populations. This research paper examines the classification of insect images into 10 species groups using the EfficientN etB3 transfer learning model. The study employs the ‘Insect Village Classification’ dataset, which consists of 10,000 labelled images representing 10 unique insect species, namely Bee, Beetle, Butterfly, Cicada, Dragonfly, Grasshopper, Moth, Scorpion, Snail, and Spider. The dataset is partitioned into three subsets, consisting of 8000, 1000, and 1000 photos. The EfficientNetB3 model demonstrates its efficacy in classifying insect species by achieving an impressive accuracy of 99% on the test subset. The performance of the model is assessed using a confusion matrix on the test set, confirming its excellent accuracy. This study highlights the capacity of transfer learning and deep learning models to automate the identification of insect species, which has significant implications for the fields of ecology, conservation, and pest management.