A study on image processing techniques and deep learning techniques for insect identification

Vinita Abhishek Gupta, Department of Computer Applications, Bhilai Institute of Technology, Durg, (C.G.), IndiaFollow
M.V. Padmavati, Department of Computer Science and Engineering, Bhilai Institute of Technology, Durg, (C.G.), India
Ravi R. Saxena, Department of Statistics and Computer Science, Indira Gandhi Krishi Vishwavidyalaya, Raipur, (C.G.), India
Pawan Kumar Patnaik, Department of Computer Science and Engineering, Bhilai Institute of Technology, Durg, (C.G.), India
Raunak Kumar Tamrakar, Department of Applied Physics, Bhilai Institute of Technology, Durg, (C.G.), India

Abstract

Automatic identification of insects and diseases has attracted researchers for the last few years. Researchers have suggested several algorithms to get around the problems of manually identifying insects and pests. Image processing techniques and deep convolution neural networks can overcome the challenges of manual insect identification and classification. This work focused on optimizing and assessing deep convolutional neural networks for insect identification. AlexNet, MobileNetv2, ResNet-50, ResNet-101, GoogleNet, InceptionV3, SqueezeNet, ShuffleNet, DenseNet201, VGG-16 and VGG-19 are the architectures evaluated on three different datasets. In our experiments, DenseNet 201 performed well with the highest test accuracy. Regarding training time, AlexNet performed well, but ShuffleNet, SqueezeNet, and MobileNet are better alternatives for small architecture.

Recommended Citation

Gupta, Vinita Abhishek; Padmavati, M.V.; Saxena, Ravi R.; Patnaik, Pawan Kumar; and Tamrakar, Raunak Kumar (2023) "A study on image processing techniques and deep learning techniques for insect identification," Karbala International Journal of Modern Science: Vol. 9 : Iss. 2 , Article 16.
Available at: https://doi.org/10.33640/2405-609X.3289