A Comparative Deep Learning Algorithms for Agricultural Insect Recognition

In this paper, we conduct a comprehensive study on insect identification and classification. However, the existing insect datasets are made up of several categories, which is far from the demands in reality. To handle this issue, we contribute to a more challenging insect image dataset, which contains 1848 images covering different pests from 118 classes. We further conduct fine-tuning on seven deep convolution neural networks, including VGG16, ResNet50, DenseNet121, Res2Net50_26w_ 4s, SCNet50_ vld, GhostNet, and RegNet. Finally, extensive experiments of the aforementioned networks on our dataset illustrate that the last four state-of-the-art deep convolution neural networks can achieve promising performance on pest identification and classification.