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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Bibliographic Details
Published in:PeerJ. Computer science 2024-11, Vol.10, p.e2406, Article e2406
Main Authors: Topal, Ahmet, Tunga, Burcu, Tirkolaee, Erfan Babaee
Format: Article
Language:English
Subjects:
Accuracy
Agricultural production
Artificial intelligence
Artificial neural networks
Classification
Coffee
Crop yield
Deep learning
Diseases
Enhanced multivariance product representation
High dimensional model representation
Image contrast
Image enhancement
Machine learning
Management systems
Medical imaging
Medical research
Medicine, Experimental
Neural networks
Plant disease
Plant diseases
Representations
Citations: Items that this one cites
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Summary: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.
ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.2406