Research on insect recognition method based on convolutional neural network

Fan Wu; Musheng Chen; Linlin Tang; Qingxian Wei; Xuandong Li

doi:10.1117/12.3019550

25 March 2024 Research on insect recognition method based on convolutional neural network

Fan Wu, Musheng Chen, Linlin Tang, Qingxian Wei, Xuandong Li

Proceedings Volume 13089, Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023); 130891G (2024) https://doi.org/10.1117/12.3019550
Event: Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023), 2023, Suzhou, China

Abstract

Agricultural and forestry injurious insects have the characteristics of numerous varieties, huge adverse effects and strong explosive power, which has a great impact on the growth of crops. Therefore, it is of great significance to correctly identify insects and give their characteristics and killing methods. A insect recognition method based on convolutional neural network is studied in this paper. Firstly, the network structures AlexNet and ResNet of the convolutional neural network are built and analyse. Then, by training and testing through datasets of seven types of insect, the results show that the ResNet network with better recognition effect is selected, whose recognition accuracy of the testing dataset reaches 96.2%.At last,by rotating, scaling, damaging and blurring the photos of part of the testing dataset, it is found that the average recognition accuracy of ResNet reaches 94.67%, indicating that ResNet has strong anti-interference ability and can be used as an effective insect recognition algorithm.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fan Wu, Musheng Chen, Linlin Tang, Qingxian Wei, and Xuandong Li "Research on insect recognition method based on convolutional neural network", Proc. SPIE 13089, Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023), 130891G (25 March 2024); https://doi.org/10.1117/12.3019550

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available