Deep convolutional neural network model for classifying common bean leaf diseases
Abstract Common bean is one of the most important crops used by Ethiopian farmers for export and local consumption. However, the quality and quantity of this crop are heavily affected by different leaf diseases and affect crop growth. Currently, common bean disease detection is performed through exp...
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| Format: | Article |
| Language: | English |
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Springer
2024-11-01
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| Series: | Discover Artificial Intelligence |
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| Online Access: | https://doi.org/10.1007/s44163-024-00212-6 |
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| _version_ | 1846147606924754944 |
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| author | Dagne Walle Girmaw Tsehay Wasihun Muluneh |
| author_facet | Dagne Walle Girmaw Tsehay Wasihun Muluneh |
| author_sort | Dagne Walle Girmaw |
| collection | DOAJ |
| description | Abstract Common bean is one of the most important crops used by Ethiopian farmers for export and local consumption. However, the quality and quantity of this crop are heavily affected by different leaf diseases and affect crop growth. Currently, common bean disease detection is performed through expert visual observation. Disease detection through observation is costly, time-consuming, and inaccurate. As a result, in this paper, a novel deep convolutional neural network model is proposed for the automatic identification of common bean leaf diseases. This research mainly focuses on the identification of three common bean leaf diseases, such as common bean brown leaf spot, common bean leaf rust, and common bean leaf beetle. The proposed model was trained using a total of 1766 images of diseased and healthy common bean leaves. The proposed model has 12 convolutional layers. The classes of the diseases were classified using a softmax classifier. The proposed model achieved a training accuracy of 98%, a validation accuracy of 97.2%, and a testing accuracy of 96%. We retrained the pre-trained models (AlexNet, GoogleNet, VGG16) in a similar simulation environment to make a comparison with the proposed model. Therefore, the proposed model achieved better performance than the pre-trained models. |
| format | Article |
| id | doaj-art-9423ced153c7482a93d0bb1bd4ac8c27 |
| institution | Kabale University |
| issn | 2731-0809 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-9423ced153c7482a93d0bb1bd4ac8c272024-12-01T12:36:34ZengSpringerDiscover Artificial Intelligence2731-08092024-11-014112410.1007/s44163-024-00212-6Deep convolutional neural network model for classifying common bean leaf diseasesDagne Walle Girmaw0Tsehay Wasihun Muluneh1Department of Information Technology, Haramaya UniversityDepartment of Information Technology, University of GondarAbstract Common bean is one of the most important crops used by Ethiopian farmers for export and local consumption. However, the quality and quantity of this crop are heavily affected by different leaf diseases and affect crop growth. Currently, common bean disease detection is performed through expert visual observation. Disease detection through observation is costly, time-consuming, and inaccurate. As a result, in this paper, a novel deep convolutional neural network model is proposed for the automatic identification of common bean leaf diseases. This research mainly focuses on the identification of three common bean leaf diseases, such as common bean brown leaf spot, common bean leaf rust, and common bean leaf beetle. The proposed model was trained using a total of 1766 images of diseased and healthy common bean leaves. The proposed model has 12 convolutional layers. The classes of the diseases were classified using a softmax classifier. The proposed model achieved a training accuracy of 98%, a validation accuracy of 97.2%, and a testing accuracy of 96%. We retrained the pre-trained models (AlexNet, GoogleNet, VGG16) in a similar simulation environment to make a comparison with the proposed model. Therefore, the proposed model achieved better performance than the pre-trained models.https://doi.org/10.1007/s44163-024-00212-6Common bean leaf diseaseCNNSegmentationSoftmax |
| spellingShingle | Dagne Walle Girmaw Tsehay Wasihun Muluneh Deep convolutional neural network model for classifying common bean leaf diseases Discover Artificial Intelligence Common bean leaf disease CNN Segmentation Softmax |
| title | Deep convolutional neural network model for classifying common bean leaf diseases |
| title_full | Deep convolutional neural network model for classifying common bean leaf diseases |
| title_fullStr | Deep convolutional neural network model for classifying common bean leaf diseases |
| title_full_unstemmed | Deep convolutional neural network model for classifying common bean leaf diseases |
| title_short | Deep convolutional neural network model for classifying common bean leaf diseases |
| title_sort | deep convolutional neural network model for classifying common bean leaf diseases |
| topic | Common bean leaf disease CNN Segmentation Softmax |
| url | https://doi.org/10.1007/s44163-024-00212-6 |
| work_keys_str_mv | AT dagnewallegirmaw deepconvolutionalneuralnetworkmodelforclassifyingcommonbeanleafdiseases AT tsehaywasihunmuluneh deepconvolutionalneuralnetworkmodelforclassifyingcommonbeanleafdiseases |