- Title
- Auto-identification of two Sitophilus sibling species on stored wheat using deep convolutional neural network
- Creator
- Yang, Haiying; Zhao, Huiyi; Zhang, Dufeng; Cao, Yang; Teng, Shyh; Pang, Shaoning; Zhou, Xiaoguang; Li, Yanyu
- Date
- 2022
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/186890
- Identifier
- vital:16987
- Identifier
-
https://doi.org/10.1002/ps.6810
- Identifier
- ISBN:1526-498X (ISSN)
- Abstract
- BACKGROUND: Sitophilus oryzae and Sitophilus zeamais are the two main insect pests that infest stored grain worldwide. Accurate and rapid identification of the two pests is challenging because of their similar appearances. The S. zeamais adults are darker and shinier than S. oryzae in visible light. Convolutional neural network (CNN) can be applied for the effective differentiation due to its high effectiveness in object recognition. RESULTS: We propose a multilayer convolutional structure (MCS) feature extractor to extract insect characteristics within each layer of the CNN architecture. A region proposal network is adopted to determine the location of a potential pest in the wheat background. The precision of classification and the robustness of bounding box regression are increased by including deeper layer variables into the classification and bounding box regression subnets, as well as combining loss functions softmax and smooth L1. The proposed multilayer convolutional structure network (MCSNet) achieves the mean average precision of 87.89 ± 2.36% from the laboratory test, with an average detection speed of 0.182 ± 0.005 s per test. The model was further assessed with the field trials, and the obtained accuracy was 90.35 ± 3.12%. For all test conditions, the average precision for S. oryzae was higher than that for S. zeamais. CONCLUSION: The proposed MCSNet model has demonstrated that it is a fast and accurate method for detecting sibling species from visible light images in both laboratory and field trials. This will ultimately be applied for pest management together with an upgraded industrial camera system, which has been installed in over 100 000 grain depots of China. © 2022 Society of Chemical Industry.
- Publisher
- John Wiley and Sons Ltd
- Relation
- Pest Management Science Vol. 78, no. 5 (2022), p. 1925-1937
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2022 Society of Chemical Industry
- Subject
- 3004 Crop and pasture production; 3109 Zoology; 4104 Environmental management; Insect detection; Multilayer convolutional structure; Region proposal network; Sitophilus oryzae; Sitophilus zeamais; Wheat background
- Reviewed
- Funder
- The authors would like to acknowledge the support of the National Common Key Grain Research and Development Plan of China (Project No.: 2017YFD0401005) established and supported under the Ministry of Science and Technology of the People’s Republic of China.
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