d the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft. Data Availability The following information was supplied regarding data availability: The data, algorithms and code are available at GitHub and Zenodo: - https://github.com/ArticleCodeHub/DeepEMPR - Topal, A. (2024). DeepEMPR. Zenodo. https://doi.org/10.5281/zenodo.13823450 . The code is available in the Supplemental File . The data is also available at Figshare: Topal, Ahmet (2024). LeafData.zip. figshare. Dataset. https://doi.org/10.6084/m9.figshare.26060464.v1 . References Agarwal et al. (2020) Agarwal M Singh A Arj
Open resource ↗ArticleCodeHub/DeepEMPR · lines:660-763Unverified paper record
DeepEMPR: coffee leaf disease detection with deep learning and enhanced multivariance product representation.
PeerJ. Computer science · 13 Nov 2024 · 10.7717/peerj-cs.2406
Abstract
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.
Plant phenotyping relevance
コーヒー葉画像から病害の種類と重症度を推定する画像・深層学習手法が研究の中心であり、植物状態の表現型評価に該当する。
abstractThis study focuses on enhancing the classification of diseases and estimating their severity in coffee leaf images.
abstractwe 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
abstractall models perform well in predicting disease severity levels, with accuracies exceeding 85%.
Code and data availability
The authors explicitly state that the data, algorithms, and code for the DeepEMPR coffee leaf disease detection study are publicly available on GitHub and Zenodo, and the leaf image dataset (LeafData.zip) is deposited on Figshare. These are paper-specific, publicly actionable assets directly supporting the study's phen
gures and/or tables, authored or reviewed drafts of the article, and approved the final draft. Data Availability The following information was supplied regarding data availability: The data, algorithms and code are available at GitHub and Zenodo: - https://github.com/ArticleCodeHub/DeepEMPR - Topal, A. (2024). DeepEMPR. Zenodo. https://doi.org/10.5281/zenodo.13823450 . The code is available in the Supplemental File . The data is also available at Figshare: Topal, Ahmet (2024). LeafData.zip. figshare. Dataset. https://doi.org/10.6084/m9.figshare.26060464.v1 . References Agarwal et al. (2020) Agarwal M Singh A Arjaria S Sinha A Gupta S 2020 ToLeD: tomato leaf disease detection using convoluti
Open resource ↗10.5281/zenodo.13823450 · lines:660-763data, algorithms and code are available at GitHub and Zenodo: - https://github.com/ArticleCodeHub/DeepEMPR - Topal, A. (2024). DeepEMPR. Zenodo. https://doi.org/10.5281/zenodo.13823450 . The code is available in the Supplemental File . The data is also available at Figshare: Topal, Ahmet (2024). LeafData.zip. figshare. Dataset. https://doi.org/10.6084/m9.figshare.26060464.v1 . References Agarwal et al. (2020) Agarwal M Singh A Arjaria S Sinha A Gupta S 2020 ToLeD: tomato leaf disease detection using convolution neural network Procedia Computer Science 167 293 301 10.1016/j.procs.2020.03.225 Ahmed et al. (2019) Ahmed K Shahidi TR Alam SMI Momen S 2019 Rice leaf disease detection using machine
Open resource ↗10.6084/m9.figshare.26060464.v1 · lines:660-763This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.