A severity estimation method for lightweight cucumber leaf disease based on DM-BiSeNet
Accurately estimating the severity of cucumber diseases is crucial for improving cucumber quality and minimizing economic losses. Deep learning techniques have shown promising results in automatically extracting disease image features for severity estimation. However, existing methods still face cha...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Information Processing in Agriculture |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214317324000209 |
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| author | Kaiyu Li Yuzhaobi Song Xinyi Zhu Lingxian Zhang |
| author_facet | Kaiyu Li Yuzhaobi Song Xinyi Zhu Lingxian Zhang |
| author_sort | Kaiyu Li |
| collection | DOAJ |
| description | Accurately estimating the severity of cucumber diseases is crucial for improving cucumber quality and minimizing economic losses. Deep learning techniques have shown promising results in automatically extracting disease image features for severity estimation. However, existing methods still face challenges in accurately estimating disease severity under complex backgrounds and achieving real-time performance.This paper presents a lightweight severity estimation method called DM-BiSeNet to address these challenges. The proposed method utilizes BiSeNet V2 as the base network and incorporates depthwise separable convolutional blocks to optimize the detail branch. A simplified MobileNet V3 network is also constructed to optimize the semantic branch. The model training process is accelerated using the AdamW optimizer. To evaluate the performance of DM-BiSeNet, a dataset consisting of cucumber powdery mildew and downy mildew disease images collected in natural scenes is utilized. Experimental results demonstrate that DM-BiSeNet achieves higher accuracy in severity estimation, with an R2 value of 0.9407 and an RMSE of 1.0680, outperforming the comparison methods. Moreover, DM-BiSeNet exhibits a complexity of 1.54 GFLOPs and is capable of reasoning 94 disease images per second.The proposed DM-BiSeNet model offers a lightweight and effective solution for accurate and rapid severity estimation of cucumber diseases under complex backgrounds. It provides a valuable technical tool for quantitative disease estimation, offering significant potential for practical applications. |
| format | Article |
| id | doaj-art-498dcb8d26ec48cfb73c5ae2e51612d3 |
| institution | DOAJ |
| issn | 2214-3173 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Information Processing in Agriculture |
| spelling | doaj-art-498dcb8d26ec48cfb73c5ae2e51612d32025-08-20T02:47:27ZengElsevierInformation Processing in Agriculture2214-31732025-03-01121687910.1016/j.inpa.2024.03.003A severity estimation method for lightweight cucumber leaf disease based on DM-BiSeNetKaiyu Li0Yuzhaobi Song1Xinyi Zhu2Lingxian Zhang3China Agricultural University, Beijing 100083, ChinaChina Agricultural University, Beijing 100083, ChinaChina Agricultural University, Beijing 100083, ChinaChina Agricultural University, Beijing 100083, China; Key Laboratory of Agricultural Informationization Standardization, Ministry of Agriculture and Rural Affairs, Beijing 100083, China; Corresponding author at: College of Information and Electrical Engineering, China Agricultural University, No.17 Qinghua Donglu, Haidian District, Beijing 100083, China.Accurately estimating the severity of cucumber diseases is crucial for improving cucumber quality and minimizing economic losses. Deep learning techniques have shown promising results in automatically extracting disease image features for severity estimation. However, existing methods still face challenges in accurately estimating disease severity under complex backgrounds and achieving real-time performance.This paper presents a lightweight severity estimation method called DM-BiSeNet to address these challenges. The proposed method utilizes BiSeNet V2 as the base network and incorporates depthwise separable convolutional blocks to optimize the detail branch. A simplified MobileNet V3 network is also constructed to optimize the semantic branch. The model training process is accelerated using the AdamW optimizer. To evaluate the performance of DM-BiSeNet, a dataset consisting of cucumber powdery mildew and downy mildew disease images collected in natural scenes is utilized. Experimental results demonstrate that DM-BiSeNet achieves higher accuracy in severity estimation, with an R2 value of 0.9407 and an RMSE of 1.0680, outperforming the comparison methods. Moreover, DM-BiSeNet exhibits a complexity of 1.54 GFLOPs and is capable of reasoning 94 disease images per second.The proposed DM-BiSeNet model offers a lightweight and effective solution for accurate and rapid severity estimation of cucumber diseases under complex backgrounds. It provides a valuable technical tool for quantitative disease estimation, offering significant potential for practical applications.http://www.sciencedirect.com/science/article/pii/S2214317324000209Cucumber diseaseSeverity estimationDepthwise separable convolutionReal-time semantic segmentation |
| spellingShingle | Kaiyu Li Yuzhaobi Song Xinyi Zhu Lingxian Zhang A severity estimation method for lightweight cucumber leaf disease based on DM-BiSeNet Information Processing in Agriculture Cucumber disease Severity estimation Depthwise separable convolution Real-time semantic segmentation |
| title | A severity estimation method for lightweight cucumber leaf disease based on DM-BiSeNet |
| title_full | A severity estimation method for lightweight cucumber leaf disease based on DM-BiSeNet |
| title_fullStr | A severity estimation method for lightweight cucumber leaf disease based on DM-BiSeNet |
| title_full_unstemmed | A severity estimation method for lightweight cucumber leaf disease based on DM-BiSeNet |
| title_short | A severity estimation method for lightweight cucumber leaf disease based on DM-BiSeNet |
| title_sort | severity estimation method for lightweight cucumber leaf disease based on dm bisenet |
| topic | Cucumber disease Severity estimation Depthwise separable convolution Real-time semantic segmentation |
| url | http://www.sciencedirect.com/science/article/pii/S2214317324000209 |
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