DeepEMPR: coffee leaf disease detection with deep learning and enhanced multivariance product representation
Plant diseases threaten agricultural sustainability by reducing crop yields. Rapid and accurate disease identification is crucial for effective management. Recent advancements in artificial intelligence (AI) have facilitated the development of automated systems for disease detection. This study focu...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-11-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2406.pdf |
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| author | Ahmet Topal Burcu Tunga Erfan Babaee Tirkolaee |
| author_facet | Ahmet Topal Burcu Tunga Erfan Babaee Tirkolaee |
| author_sort | Ahmet Topal |
| collection | DOAJ |
| description | Plant diseases threaten agricultural sustainability by reducing crop yields. Rapid and accurate disease identification is crucial for effective management. Recent advancements in artificial intelligence (AI) have facilitated the development of automated systems for disease detection. This study focuses on enhancing the classification of diseases and estimating their severity in coffee leaf images. To do so, we propose a novel approach as the preprocessing step for the classification in which enhanced multivariance product representation (EMPR) is used to decompose the considered image into components, a new image is constructed using some of those components, and the contrast of the new image is enhanced by applying high-dimensional model representation (HDMR) to highlight the diseased parts of the leaves. Popular convolutional neural network (CNN) architectures, including AlexNet, VGG16, and ResNet50, are evaluated. Results show that VGG16 achieves the highest classification accuracy of approximately 96%, while all models perform well in predicting disease severity levels, with accuracies exceeding 85%. Notably, the ResNet50 model achieves accuracy levels surpassing 90%. This research contributes to the advancement of automated crop health management systems. |
| format | Article |
| id | doaj-art-8f1950ce5cc643f4a49e8b844a91173d |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-8f1950ce5cc643f4a49e8b844a91173d2024-11-15T15:05:15ZengPeerJ Inc.PeerJ Computer Science2376-59922024-11-0110e240610.7717/peerj-cs.2406DeepEMPR: coffee leaf disease detection with deep learning and enhanced multivariance product representationAhmet Topal0Burcu Tunga1Erfan Babaee Tirkolaee2Department of Mathematics Engineering, Istanbul Technical University, Istanbul, TurkeyDepartment of Mathematics Engineering, Istanbul Technical University, Istanbul, TurkeyDepartment of Industrial Engineering, Istinye University, Istanbul, TurkeyPlant diseases threaten agricultural sustainability by reducing crop yields. Rapid and accurate disease identification is crucial for effective management. Recent advancements in artificial intelligence (AI) have facilitated the development of automated systems for disease detection. This study focuses on enhancing the classification of diseases and estimating their severity in coffee leaf images. To do so, we propose a novel approach as the preprocessing step for the classification in which enhanced multivariance product representation (EMPR) is used to decompose the considered image into components, a new image is constructed using some of those components, and the contrast of the new image is enhanced by applying high-dimensional model representation (HDMR) to highlight the diseased parts of the leaves. Popular convolutional neural network (CNN) architectures, including AlexNet, VGG16, and ResNet50, are evaluated. Results show that VGG16 achieves the highest classification accuracy of approximately 96%, while all models perform well in predicting disease severity levels, with accuracies exceeding 85%. Notably, the ResNet50 model achieves accuracy levels surpassing 90%. This research contributes to the advancement of automated crop health management systems.https://peerj.com/articles/cs-2406.pdfPlant diseaseDeep learningEnhanced multivariance product representationHigh dimensional model representation |
| spellingShingle | Ahmet Topal Burcu Tunga Erfan Babaee Tirkolaee DeepEMPR: coffee leaf disease detection with deep learning and enhanced multivariance product representation PeerJ Computer Science Plant disease Deep learning Enhanced multivariance product representation High dimensional model representation |
| title | DeepEMPR: coffee leaf disease detection with deep learning and enhanced multivariance product representation |
| title_full | DeepEMPR: coffee leaf disease detection with deep learning and enhanced multivariance product representation |
| title_fullStr | DeepEMPR: coffee leaf disease detection with deep learning and enhanced multivariance product representation |
| title_full_unstemmed | DeepEMPR: coffee leaf disease detection with deep learning and enhanced multivariance product representation |
| title_short | DeepEMPR: coffee leaf disease detection with deep learning and enhanced multivariance product representation |
| title_sort | deepempr coffee leaf disease detection with deep learning and enhanced multivariance product representation |
| topic | Plant disease Deep learning Enhanced multivariance product representation High dimensional model representation |
| url | https://peerj.com/articles/cs-2406.pdf |
| work_keys_str_mv | AT ahmettopal deepemprcoffeeleafdiseasedetectionwithdeeplearningandenhancedmultivarianceproductrepresentation AT burcutunga deepemprcoffeeleafdiseasedetectionwithdeeplearningandenhancedmultivarianceproductrepresentation AT erfanbabaeetirkolaee deepemprcoffeeleafdiseasedetectionwithdeeplearningandenhancedmultivarianceproductrepresentation |