Pep-VGGNet: A Novel Transfer Learning Method for Pepper Leaf Disease Diagnosis
The health of crops is a major challenge for productivity growth in agriculture, with plant diseases playing a key role in limiting crop yield. Identifying and understanding these diseases is crucial to preventing their spread. In particular, greenhouse pepper leaves are susceptible to diseases such...
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MDPI AG
2025-08-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/15/8690 |
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| author | Süleyman Çetinkaya Amira Tandirovic Gursel |
| author_facet | Süleyman Çetinkaya Amira Tandirovic Gursel |
| author_sort | Süleyman Çetinkaya |
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| description | The health of crops is a major challenge for productivity growth in agriculture, with plant diseases playing a key role in limiting crop yield. Identifying and understanding these diseases is crucial to preventing their spread. In particular, greenhouse pepper leaves are susceptible to diseases such as mildew, mites, caterpillars, aphids, and blight, which leave distinctive marks that can be used for disease classification. The study proposes a seven-class classifier for the rapid and accurate diagnosis of pepper diseases, with a primary focus on pre-processing techniques to enhance colour differentiation between green and yellow shades, thereby facilitating easier classification among the classes. A novel algorithm is introduced to improve image vibrancy, contrast, and colour properties. The diagnosis is performed using a modified VGG16Net model, which includes three additional layers for fine-tuning. After initialising on the ImageNet dataset, some layers are frozen to prevent redundant learning. The classification is additionally accelerated by introducing flattened, dense, and dropout layers. The proposed model is tested on a private dataset collected specifically for this study. Notably, this work is the first to focus on diagnosing aphid and caterpillar diseases in peppers. The model achieves an average accuracy of 92.00%, showing promising potential for seven-class deep learning-based disease diagnostics. Misclassifications in the aphid class are primarily due to the limited number of samples available. |
| format | Article |
| id | doaj-art-b60586c4f182406e8d3536447e194347 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-b60586c4f182406e8d3536447e1943472025-08-20T03:36:35ZengMDPI AGApplied Sciences2076-34172025-08-011515869010.3390/app15158690Pep-VGGNet: A Novel Transfer Learning Method for Pepper Leaf Disease DiagnosisSüleyman Çetinkaya0Amira Tandirovic Gursel1Department of Electrical and Electronics Engineering, Adana Alparslan Türkeş Science and Technology University, 01250 Sarıçam, Adana, TurkeyDepartment of Electrical and Electronics Engineering, Adana Alparslan Türkeş Science and Technology University, 01250 Sarıçam, Adana, TurkeyThe health of crops is a major challenge for productivity growth in agriculture, with plant diseases playing a key role in limiting crop yield. Identifying and understanding these diseases is crucial to preventing their spread. In particular, greenhouse pepper leaves are susceptible to diseases such as mildew, mites, caterpillars, aphids, and blight, which leave distinctive marks that can be used for disease classification. The study proposes a seven-class classifier for the rapid and accurate diagnosis of pepper diseases, with a primary focus on pre-processing techniques to enhance colour differentiation between green and yellow shades, thereby facilitating easier classification among the classes. A novel algorithm is introduced to improve image vibrancy, contrast, and colour properties. The diagnosis is performed using a modified VGG16Net model, which includes three additional layers for fine-tuning. After initialising on the ImageNet dataset, some layers are frozen to prevent redundant learning. The classification is additionally accelerated by introducing flattened, dense, and dropout layers. The proposed model is tested on a private dataset collected specifically for this study. Notably, this work is the first to focus on diagnosing aphid and caterpillar diseases in peppers. The model achieves an average accuracy of 92.00%, showing promising potential for seven-class deep learning-based disease diagnostics. Misclassifications in the aphid class are primarily due to the limited number of samples available.https://www.mdpi.com/2076-3417/15/15/8690pepper diseasesdiagnosiscolour enhancementdeep learningpre-trained VGG16 model |
| spellingShingle | Süleyman Çetinkaya Amira Tandirovic Gursel Pep-VGGNet: A Novel Transfer Learning Method for Pepper Leaf Disease Diagnosis Applied Sciences pepper diseases diagnosis colour enhancement deep learning pre-trained VGG16 model |
| title | Pep-VGGNet: A Novel Transfer Learning Method for Pepper Leaf Disease Diagnosis |
| title_full | Pep-VGGNet: A Novel Transfer Learning Method for Pepper Leaf Disease Diagnosis |
| title_fullStr | Pep-VGGNet: A Novel Transfer Learning Method for Pepper Leaf Disease Diagnosis |
| title_full_unstemmed | Pep-VGGNet: A Novel Transfer Learning Method for Pepper Leaf Disease Diagnosis |
| title_short | Pep-VGGNet: A Novel Transfer Learning Method for Pepper Leaf Disease Diagnosis |
| title_sort | pep vggnet a novel transfer learning method for pepper leaf disease diagnosis |
| topic | pepper diseases diagnosis colour enhancement deep learning pre-trained VGG16 model |
| url | https://www.mdpi.com/2076-3417/15/15/8690 |
| work_keys_str_mv | AT suleymancetinkaya pepvggnetanoveltransferlearningmethodforpepperleafdiseasediagnosis AT amiratandirovicgursel pepvggnetanoveltransferlearningmethodforpepperleafdiseasediagnosis |