Classification of Bitter gourd leaf disease using deep learning architecture ResNet50
The primary goal of this research is to develop a feasible and efficient method for identifying the disease and to advocate for an appropriate system that provides an early and cost-effective solution to this problem. Due to their superior computational capabilities and accuracy, computer vision and...
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| Format: | Article |
| Language: | English |
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Universitas Ahmad Dahlan
2025-05-01
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| Series: | IJAIN (International Journal of Advances in Intelligent Informatics) |
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| Online Access: | https://ijain.org/index.php/IJAIN/article/view/1925 |
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| author | Artika Artika Wikky Fawwaz Al Maki |
| author_facet | Artika Artika Wikky Fawwaz Al Maki |
| author_sort | Artika Artika |
| collection | DOAJ |
| description | The primary goal of this research is to develop a feasible and efficient method for identifying the disease and to advocate for an appropriate system that provides an early and cost-effective solution to this problem. Due to their superior computational capabilities and accuracy, computer vision and machine learning methods and techniques have garnered significant attention in recent years for classifying various leaf diseases. As a result, Resnet50 and Resnet101 were proposed in this study for the classification of bitter gourd disease. The 2490 images of bitter gourd leaves are classified into three categories: Healthy leaf, Fusarium Wilt leaf, and Yellow Mosaic leaf. The proposed ResNet50 architecture accomplished 98% accuracy with the Adam optimizer. The ResNet101 architecture achieves an average accuracy of 94% with the Adam optimizer. As a result, the proposed model can differentiate between healthy and diseased bitter gourd leaves. This research contributes to the development of methods for detecting bitter melon leaf disease using computer vision and machine learning, achieving high accuracy and supporting automatic disease diagnosis. The results can help farmers quickly and cost-effectively detect diseases early, thereby increasing agricultural productivity. |
| format | Article |
| id | doaj-art-5c7a8a361d93491281f1ed330ad8472f |
| institution | DOAJ |
| issn | 2442-6571 2548-3161 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Universitas Ahmad Dahlan |
| record_format | Article |
| series | IJAIN (International Journal of Advances in Intelligent Informatics) |
| spelling | doaj-art-5c7a8a361d93491281f1ed330ad8472f2025-08-20T03:17:39ZengUniversitas Ahmad DahlanIJAIN (International Journal of Advances in Intelligent Informatics)2442-65712548-31612025-05-0111232433510.26555/ijain.v11i2.1925337Classification of Bitter gourd leaf disease using deep learning architecture ResNet50Artika Artika0Wikky Fawwaz Al Maki1Telkom UniversityTelkom UniversityThe primary goal of this research is to develop a feasible and efficient method for identifying the disease and to advocate for an appropriate system that provides an early and cost-effective solution to this problem. Due to their superior computational capabilities and accuracy, computer vision and machine learning methods and techniques have garnered significant attention in recent years for classifying various leaf diseases. As a result, Resnet50 and Resnet101 were proposed in this study for the classification of bitter gourd disease. The 2490 images of bitter gourd leaves are classified into three categories: Healthy leaf, Fusarium Wilt leaf, and Yellow Mosaic leaf. The proposed ResNet50 architecture accomplished 98% accuracy with the Adam optimizer. The ResNet101 architecture achieves an average accuracy of 94% with the Adam optimizer. As a result, the proposed model can differentiate between healthy and diseased bitter gourd leaves. This research contributes to the development of methods for detecting bitter melon leaf disease using computer vision and machine learning, achieving high accuracy and supporting automatic disease diagnosis. The results can help farmers quickly and cost-effectively detect diseases early, thereby increasing agricultural productivity.https://ijain.org/index.php/IJAIN/article/view/1925bitter gourd leafdeep learningimage processingresnet50resnet101 |
| spellingShingle | Artika Artika Wikky Fawwaz Al Maki Classification of Bitter gourd leaf disease using deep learning architecture ResNet50 IJAIN (International Journal of Advances in Intelligent Informatics) bitter gourd leaf deep learning image processing resnet50 resnet101 |
| title | Classification of Bitter gourd leaf disease using deep learning architecture ResNet50 |
| title_full | Classification of Bitter gourd leaf disease using deep learning architecture ResNet50 |
| title_fullStr | Classification of Bitter gourd leaf disease using deep learning architecture ResNet50 |
| title_full_unstemmed | Classification of Bitter gourd leaf disease using deep learning architecture ResNet50 |
| title_short | Classification of Bitter gourd leaf disease using deep learning architecture ResNet50 |
| title_sort | classification of bitter gourd leaf disease using deep learning architecture resnet50 |
| topic | bitter gourd leaf deep learning image processing resnet50 resnet101 |
| url | https://ijain.org/index.php/IJAIN/article/view/1925 |
| work_keys_str_mv | AT artikaartika classificationofbittergourdleafdiseaseusingdeeplearningarchitectureresnet50 AT wikkyfawwazalmaki classificationofbittergourdleafdiseaseusingdeeplearningarchitectureresnet50 |