LAUSR

Abstract The application of deep neural networks in computer vision has remarkably improved the reliability of automated insect pest identification algorithms. However, to build a robust deep neural network classifier model, sufficient number of image data is necessary. In preparing image data, collection and annotation of large datasets can be labor-intensive and time-consuming. This work aims to reduce the effort and time needed to perform such tasks. In this research, a method for synthesizing insect pest training images in sticky paper trap images and enhancing the performance of insect pest convolutional neural network (CNN) classifier models through a generative adversarial network (GAN) is proposed. Through GAN, images were synthesized to balance and increase the number of training images even with limited data. The fidelity of the synthetic images was tested using t-SNE visualization. To evaluate the developed network, the minimum number of images required to apply GAN-based augmentation was determined. Furthermore, the performance of the insect pest classifier models without augmentation, with traditional augmentation, and with GAN-based augmentation were compared. Testing results show that the classifier models trained with images augmented using GAN achieved an F1-score of 0.95, outperforming the models trained with traditionally augmented images with an F1-score of 0.92. This research shows that by using GAN-based augmentation method, the insect pest CNN classification models can yield a better performance compared to classic augmentation method and reduce the effort needed for data collection. Furthermore, the proposed approach can be easily adapted to various agricultural applications using deep neural networks when image data is limited.