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Context-Aware Systems and Applications. 11th EAI International Conference, ICCASA 2022, Vinh Long, Vietnam, October 27-28, 2022, Proceedings

Research Article

Identification of Abnormal Cucumber Leaves Image Based on Recurrent Residual U-Net and Support Vector Machine Techniques

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-28816-6_7,
        author={Nguyen Thanh Binh and Nguyen Kim Quyen},
        title={Identification of Abnormal Cucumber Leaves Image Based on Recurrent Residual U-Net and Support Vector Machine Techniques},
        proceedings={Context-Aware Systems and Applications. 11th EAI International Conference, ICCASA 2022, Vinh Long, Vietnam, October 27-28, 2022, Proceedings},
        proceedings_a={ICCASA},
        year={2023},
        month={3},
        keywords={Cucumber leaf diseases Recurrent residual U-Net SVM Identify diseases},
        doi={10.1007/978-3-031-28816-6_7}
    }
    
  • Nguyen Thanh Binh
    Nguyen Kim Quyen
    Year: 2023
    Identification of Abnormal Cucumber Leaves Image Based on Recurrent Residual U-Net and Support Vector Machine Techniques
    ICCASA
    Springer
    DOI: 10.1007/978-3-031-28816-6_7
Nguyen Thanh Binh1,*, Nguyen Kim Quyen2
  • 1: Department of Information Systems, Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, 268 Ly Thuong Kiet Street
  • 2: Faculty of Agriculture and Fishery
*Contact email: ntbinh@hcmut.edu.vn

Abstract

Cucumber diseases arise and spread quickly, affecting the yield and quality of cucumbers. The correct diagnosis of diseases on cucumber leaves is an important factor determining the success of control measures. To support accurate identification of cucumber leaf diseases, we proposed a machine learning method to identify powdery mildew diseases, downy mildew diseases, blight diseases, and anthracnose diseases on cucumber leaves. Most of the features of these diseases are similar. Therefore, the automatic identification of these diseases presents many challenges. The proposed method uses the recurrent residual U-Net deep learning model and the traditional support vector machine technique to identify diseases on cucumber leaves with an average accuracy of 96.33%, higher than other methods.

Keywords
Cucumber leaf diseases Recurrent residual U-Net SVM Identify diseases
Published
2023-03-24
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-28816-6_7
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