Home > Published Issues > 2023 > Volume 14, No. 2, 2023 >
JAIT 2023 Vol.14(2): 328-341
doi: 10.12720/jait.14.2.328-341

Large-Scale Insect Pest Image Classification

Thanh-Nghi Doan 1,2
1. Faculty of Information Technology, An Giang University, An Giang, Vietnam
2. Vietnam National University Ho Chi Minh City, Vietnam
Email: dtnghi@agu.edu.vn

Manuscript received October 13, 2022; revised October 30, 2022; accepted December 19, 2022; published April 17, 2023.

Abstract—One of the main issues with agricultural production is insect attack, which leads to poor crop quality. Farmers, however, have a complicated and time-consuming task in detecting and categorizing insects. Therefore, research on an effective system for image-based automated insect classification is crucial. The conventional “softmax” function is utilized to determine the category for new image occurrences and minimize “cross-entropy” loss in the bulk of current research, which focuses on employing deep convolutional neural networks to categorize insect images. This paper presents a novel method for large-scale insect pest image classification by combining fine-tuning EfficientNets and Power Mean Support Vector Machine (SVM). First, EfficientNet models are fine-tuned and re-trained on new insect pest image datasets. The retrieved features from EfficientNet models are then utilized to create a machine learning classifier. In the network’s classification stage, the traditional “softmax” function is substituted with a non-linear classifier, Power Mean SVM. As a result, rather than “cross-entropy loss,” the training process focuses on reducing “margin-based loss.” Several benchmark insect image datasets were used to evaluate our proposed method. According to the numerical results, our method outperforms other cutting-edge methods for large-scale insect pest image categorization. With fine-tuning EfficientNets and Power Mean SVM, the classification accuracy of the proposed method for the Xie24, D0, and IP102 large insect pest datasets is 99%, 99%, and 72.31%, respectively. To our knowledge, these are the best performing image classification results for these datasets.
Keywords—EfficientNets, power mean Support Vector Machine (SVM), large-scale insect image categorization

Cite: Thanh-Nghi Doan, "Large-Scale Insect Pest Image Classification," Journal of Advances in Information Technology, Vol. 14, No. 2, pp. 328-341, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.