Comparison Efficacy of VGG16 and VGG19 Insect Classification Models

Djarot Hindarto, Nihayah Afarini, Endah T Esthi H


This study compares two popular deep-learning models, VGG16 and VGG19, for insect classification. This study aims to evaluate insect detection architectures to automate insect identification. We use a large, heterogeneous dataset of insect species, including common pests and beneficial insects, and their images to achieve this goal. The dataset was used to re-adjust the VGG16 and VGG19 models and analyze their classification performance. With an average improvement of 1,8%, VGG19 outperforms VGG16 in insect classification accuracy. VGG19 is more robust because it can handle complex traits and subtle insect morphology differences. Each architecture's model training duration and computational resources are examined for their practicality in real-world scenarios. This study emphasizes deep learning models in insect classification and shows VGG19's higher accuracy and robustness than VGG16. These findings matter to entomologists, agricultural researchers, and pest control experts. They can improve insect identification accuracy and effectiveness using VGG19-based models, which can help solve insect-related problems in various fields.96.28 percent accuracy was achieved with the implementation of VGG16, according to the experimental findings; 97.07 percent accuracy was obtained with the implementation of VGG19.

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