DCTnet: a double-channel transformer network for peach disease detection using UAVs
Abstract The use of unmanned aerial vehicle (UAV) technology to inspect extensive peach orchards to improve fruit yield and quality is currently a major area of research. The challenge is to accurately detect peach diseases in real time, which is critical to improving peach production. The dense arr...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2024-12-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01749-w |
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| _version_ | 1832571156442382336 |
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| author | Jie Zhang Dailin Li Xiaoping Shi Fengxian Wang Linwei Li Yibin Chen |
| author_facet | Jie Zhang Dailin Li Xiaoping Shi Fengxian Wang Linwei Li Yibin Chen |
| author_sort | Jie Zhang |
| collection | DOAJ |
| description | Abstract The use of unmanned aerial vehicle (UAV) technology to inspect extensive peach orchards to improve fruit yield and quality is currently a major area of research. The challenge is to accurately detect peach diseases in real time, which is critical to improving peach production. The dense arrangement of peaches and the uneven lighting conditions significantly hamper the accuracy of disease detection. To overcome this, this paper presents a dual-channel transformer network (DCTNet) for peach disease detection. First, an Adaptive Dual-Channel Affine Transformer (ADCT) is developed to efficiently capture key information in images of diseased peaches by integrating features across spatial and channel dimensions within blocks. Next, a Robust Gated Feed Forward Network (RGFN) is constructed to extend the receptive field of the model by improving its context aggregation capabilities. Finally, a Local–Global Network is proposed to fully capture the multi-scale features of peach disease images through a collaborative training approach with input images. Furthermore, a peach disease dataset including different growth stages of peaches is constructed to evaluate the detection performance of the proposed method. Extensive experimental results show that our model outperforms other sophisticated models, achieving an $${AP}_{50}$$ AP 50 of 95.57% and an F1 score of 0.91. The integration of this method into UAV systems for surveying large peach orchards ensures accurate disease detection, thereby safeguarding peach production. |
| format | Article |
| id | doaj-art-38eca509f0f349639ac6a0e4a222f293 |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-38eca509f0f349639ac6a0e4a222f2932025-02-02T12:49:27ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-12-0111111810.1007/s40747-024-01749-wDCTnet: a double-channel transformer network for peach disease detection using UAVsJie Zhang0Dailin Li1Xiaoping Shi2Fengxian Wang3Linwei Li4Yibin Chen5Collage of Electric and Information Engineering, Zhengzhou University of Light IndustryCollage of Electric and Information Engineering, Zhengzhou University of Light IndustryControl and Simulation Center, Harbin Institute of TechnologyCollage of Electric and Information Engineering, Zhengzhou University of Light IndustryCollage of Electric and Information Engineering, Zhengzhou University of Light IndustryCollage of Electric and Information Engineering, Zhengzhou University of Light IndustryAbstract The use of unmanned aerial vehicle (UAV) technology to inspect extensive peach orchards to improve fruit yield and quality is currently a major area of research. The challenge is to accurately detect peach diseases in real time, which is critical to improving peach production. The dense arrangement of peaches and the uneven lighting conditions significantly hamper the accuracy of disease detection. To overcome this, this paper presents a dual-channel transformer network (DCTNet) for peach disease detection. First, an Adaptive Dual-Channel Affine Transformer (ADCT) is developed to efficiently capture key information in images of diseased peaches by integrating features across spatial and channel dimensions within blocks. Next, a Robust Gated Feed Forward Network (RGFN) is constructed to extend the receptive field of the model by improving its context aggregation capabilities. Finally, a Local–Global Network is proposed to fully capture the multi-scale features of peach disease images through a collaborative training approach with input images. Furthermore, a peach disease dataset including different growth stages of peaches is constructed to evaluate the detection performance of the proposed method. Extensive experimental results show that our model outperforms other sophisticated models, achieving an $${AP}_{50}$$ AP 50 of 95.57% and an F1 score of 0.91. The integration of this method into UAV systems for surveying large peach orchards ensures accurate disease detection, thereby safeguarding peach production.https://doi.org/10.1007/s40747-024-01749-wPeach disease detectionAdaptive dual-channel affine transformerRobust gated feed forward networkLocal–global network |
| spellingShingle | Jie Zhang Dailin Li Xiaoping Shi Fengxian Wang Linwei Li Yibin Chen DCTnet: a double-channel transformer network for peach disease detection using UAVs Complex & Intelligent Systems Peach disease detection Adaptive dual-channel affine transformer Robust gated feed forward network Local–global network |
| title | DCTnet: a double-channel transformer network for peach disease detection using UAVs |
| title_full | DCTnet: a double-channel transformer network for peach disease detection using UAVs |
| title_fullStr | DCTnet: a double-channel transformer network for peach disease detection using UAVs |
| title_full_unstemmed | DCTnet: a double-channel transformer network for peach disease detection using UAVs |
| title_short | DCTnet: a double-channel transformer network for peach disease detection using UAVs |
| title_sort | dctnet a double channel transformer network for peach disease detection using uavs |
| topic | Peach disease detection Adaptive dual-channel affine transformer Robust gated feed forward network Local–global network |
| url | https://doi.org/10.1007/s40747-024-01749-w |
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