Potato Plant Variety Identification Study Based on Improved Swin Transformer
Potato is one of the most important food crops in the world and occupies a crucial position in China’s agricultural development. Due to the large number of potato varieties and the phenomenon of variety mixing, the development of the potato industry is seriously affected. Therefore, accurate identif...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/1/87 |
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| author | Xue Xing Chengzhong Liu Junying Han Quan Feng Enfang Qi Yaying Qu Baixiong Ma |
| author_facet | Xue Xing Chengzhong Liu Junying Han Quan Feng Enfang Qi Yaying Qu Baixiong Ma |
| author_sort | Xue Xing |
| collection | DOAJ |
| description | Potato is one of the most important food crops in the world and occupies a crucial position in China’s agricultural development. Due to the large number of potato varieties and the phenomenon of variety mixing, the development of the potato industry is seriously affected. Therefore, accurate identification of potato varieties is a key link to promote the development of the potato industry. Deep learning technology is used to identify potato varieties with good accuracy, but there are relatively few related studies. Thus, this paper introduces an enhanced Swin Transformer classification model named MSR-SwinT (Multi-scale residual Swin Transformer). The model employs a multi-scale feature fusion module in place of patch partitioning and linear embedding. This approach effectively extracts features of various scales and enhances the model’s feature extraction capability. Additionally, the residual learning strategy is integrated into the Swin Transformer block, effectively addressing the issue of gradient disappearance and enabling the model to capture complex features more effectively. The model can better capture complex features. The enhanced MSR-SwinT model is validated using the potato plant dataset, demonstrating strong performance in potato plant image recognition with an accuracy of 94.64%. This represents an improvement of 3.02 percentage points compared to the original Swin Transformer model. Experimental evidence shows that the improved model performs better and generalizes better, providing a more effective solution for potato variety identification. |
| format | Article |
| id | doaj-art-025fdbe382024234bc72bc0738615002 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-025fdbe382024234bc72bc07386150022025-01-10T13:13:39ZengMDPI AGAgriculture2077-04722025-01-011518710.3390/agriculture15010087Potato Plant Variety Identification Study Based on Improved Swin TransformerXue Xing0Chengzhong Liu1Junying Han2Quan Feng3Enfang Qi4Yaying Qu5Baixiong Ma6College of Information Sciences and Technology, Gansu Agricultural University, Lanzhou 730070, ChinaCollege of Information Sciences and Technology, Gansu Agricultural University, Lanzhou 730070, ChinaCollege of Information Sciences and Technology, Gansu Agricultural University, Lanzhou 730070, ChinaCollege of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, ChinaPotato Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou 730070, ChinaPotato Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou 730070, ChinaCollege of Information Sciences and Technology, Gansu Agricultural University, Lanzhou 730070, ChinaPotato is one of the most important food crops in the world and occupies a crucial position in China’s agricultural development. Due to the large number of potato varieties and the phenomenon of variety mixing, the development of the potato industry is seriously affected. Therefore, accurate identification of potato varieties is a key link to promote the development of the potato industry. Deep learning technology is used to identify potato varieties with good accuracy, but there are relatively few related studies. Thus, this paper introduces an enhanced Swin Transformer classification model named MSR-SwinT (Multi-scale residual Swin Transformer). The model employs a multi-scale feature fusion module in place of patch partitioning and linear embedding. This approach effectively extracts features of various scales and enhances the model’s feature extraction capability. Additionally, the residual learning strategy is integrated into the Swin Transformer block, effectively addressing the issue of gradient disappearance and enabling the model to capture complex features more effectively. The model can better capture complex features. The enhanced MSR-SwinT model is validated using the potato plant dataset, demonstrating strong performance in potato plant image recognition with an accuracy of 94.64%. This represents an improvement of 3.02 percentage points compared to the original Swin Transformer model. Experimental evidence shows that the improved model performs better and generalizes better, providing a more effective solution for potato variety identification.https://www.mdpi.com/2077-0472/15/1/87deep learningpotatoSwin Transformerconvolutional neural networkvariety identification |
| spellingShingle | Xue Xing Chengzhong Liu Junying Han Quan Feng Enfang Qi Yaying Qu Baixiong Ma Potato Plant Variety Identification Study Based on Improved Swin Transformer Agriculture deep learning potato Swin Transformer convolutional neural network variety identification |
| title | Potato Plant Variety Identification Study Based on Improved Swin Transformer |
| title_full | Potato Plant Variety Identification Study Based on Improved Swin Transformer |
| title_fullStr | Potato Plant Variety Identification Study Based on Improved Swin Transformer |
| title_full_unstemmed | Potato Plant Variety Identification Study Based on Improved Swin Transformer |
| title_short | Potato Plant Variety Identification Study Based on Improved Swin Transformer |
| title_sort | potato plant variety identification study based on improved swin transformer |
| topic | deep learning potato Swin Transformer convolutional neural network variety identification |
| url | https://www.mdpi.com/2077-0472/15/1/87 |
| work_keys_str_mv | AT xuexing potatoplantvarietyidentificationstudybasedonimprovedswintransformer AT chengzhongliu potatoplantvarietyidentificationstudybasedonimprovedswintransformer AT junyinghan potatoplantvarietyidentificationstudybasedonimprovedswintransformer AT quanfeng potatoplantvarietyidentificationstudybasedonimprovedswintransformer AT enfangqi potatoplantvarietyidentificationstudybasedonimprovedswintransformer AT yayingqu potatoplantvarietyidentificationstudybasedonimprovedswintransformer AT baixiongma potatoplantvarietyidentificationstudybasedonimprovedswintransformer |