Integration of a Dual Hybrid Deep Convolutional Neural Network Framework for Insect Taxonomic Classification

Abstract

Insects constitute a vital element within numerous ecosystems, exerting significant influence on biodiversity, ecological dynamics, and the well-being of human health as well as natural resources. The taxonomic group "Insecta" stands out as one of the largest and most extensive within the realm of biodiversity taxonomy. Given their importance, sustainable management of insects, ecosystems, and their interrelationships is vital for the survival of all organisms. The novel approach for classifying insect images presented in this research is based on systematic taxonomic ranks at the order, family, and species levels and combining deep convolutional neural network, termed as “Dual Hybrid”. Convolutional Neural Network This model utilized a total of 6060 images for classification at the order level, 3740 images for the family level, and 1582 images for the species level. Various fine-tuned pre-trained DCNN models were employed to create the hybrid model. This proposed research work mainly focuses on increasing accuracy and efficiency of taxonomic level insect images classification and identification. Experimental results indicate promising outcomes. The DHDCNet model proposed in this research achieved classification accuracies of 98.97% for order classification, 97.37% for family classification, and 89% for species classification across five distinct insect classes. A detailed evaluation of the model's performance was conducted using metrics such as precision, recall, and F1-score, which provided valuable insights into its effectiveness across various dimensions.