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ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 15 - 2024 |
doi: 10.3389/fpls.2024.1445418
This article is part of the Research Topic UAVs for Crop Protection: Remote Sensing, Prescription Mapping and Precision Spraying View all 3 articles
Residual Swin Transformer for classifying the types of cotton pests in complex background
Provisionally accepted
Ting Zhang
1
Jikui Zhu
1*
Fengkui Zhang
1*
Shijie Zhao
1*
Wei Liu
2
Ruohong He
1*
Hongqiang Dong
1*
Qingqing Hong
2
Changwei Tan
2
Ping Li
1*- 1 Tarim University, Aral, China
- 2 Yangzhou University, Yangzhou, Jiangsu Province, China
Cotton pests have a major impact on cotton quality and yield during cotton production and cultivation. With the rapid development of agricultural intelligence, the accurate classification of cotton pests is a key factor in realizing the precise application of medicines by utilize unmanned aerial vehicles (UAVs), large application devices and other equipment. In this study, a cotton insect pest classification model based on improved Swin Transformer is proposed. The model introduces the residual module, skip connection, into Swin Transformer to improve the problem that pest features are easily confused in complex backgrounds leading to poor classification accuracy, and to enhance the recognition of cotton pests. In this study, 2705 leaf images of cotton insect pests (including three insect pests, cotton aphids, cotton mirids and cotton leaf mites) were collected in the field, and after image preprocessing and data augmentation operations, model training was performed. The test results proved that the accuracy of the improved model compared to the original model increased from 94.6% to 97.4%, and the prediction time for a single image was 0.00434s. The improved Swin Transformer model was compared with seven kinds of classification models (VGG11, VGG11-bn, Resnet18, MobilenetV2, VIT, Swin Transformer small, and Swin Transformer base), and the model accuracy was increased respectively by 0.5%, 4.7%, 2.2%, 2.5%, 6.3%, 7.9%, 8.0%. Therefore, this study demonstrates that the improved Swin Transformer model significantly improves the accuracy and efficiency of cotton pest detection compared with other classification models, and can be deployed on edge devices such as utilize unmanned aerial vehicles (UAVs), thus providing an important technological support and theoretical basis for cotton pest control and precision drug application.
Keywords: Cotton pests, swin transformer, Complex background, deep learning, unmanned aerial vehicles
Received: 07 Jun 2024; Accepted: 08 Aug 2024.
Copyright: © 2024 Zhang, Zhu, Zhang, Zhao, Liu, He, Dong, Hong, Tan and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Jikui Zhu, Tarim University, Aral, China
Fengkui Zhang, Tarim University, Aral, China
Shijie Zhao, Tarim University, Aral, China
Ruohong He, Tarim University, Aral, China
Hongqiang Dong, Tarim University, Aral, China
Ping Li, Tarim University, Aral, China
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