Version 1
: Received: 5 December 2022 / Approved: 6 December 2022 / Online: 6 December 2022 (01:30:03 CET)
How to cite:
Qumsiyeh, E.M.; Sabha, M.N. Classification of Leaf Disease via Deep Neural Network combined with Clustering Algorithm. Preprints2022, 2022120082. https://doi.org/10.20944/preprints202212.0082.v1
Qumsiyeh, E.M.; Sabha, M.N. Classification of Leaf Disease via Deep Neural Network combined with Clustering Algorithm. Preprints 2022, 2022120082. https://doi.org/10.20944/preprints202212.0082.v1
Qumsiyeh, E.M.; Sabha, M.N. Classification of Leaf Disease via Deep Neural Network combined with Clustering Algorithm. Preprints2022, 2022120082. https://doi.org/10.20944/preprints202212.0082.v1
APA Style
Qumsiyeh, E.M., & Sabha, M.N. (2022). Classification of Leaf Disease via Deep Neural Network combined with Clustering Algorithm. Preprints. https://doi.org/10.20944/preprints202212.0082.v1
Chicago/Turabian Style
Qumsiyeh, E.M. and Muath Naji Sabha. 2022 "Classification of Leaf Disease via Deep Neural Network combined with Clustering Algorithm" Preprints. https://doi.org/10.20944/preprints202212.0082.v1
Abstract
The agricultural sector in Palestine has a significant role in its economy. However, the production of this sector is affected by different kinds of plant diseases, specifically leaf diseases. Automatic agricultural leaf disease detection is essential for the early diagnosis and controls the overall health of fields. Image segmentation techniques, clustering, and deep learning are often used to detect diseased leaves. This study proposes a novel hybrid approach based on image classification. The hybrid approach combines the k-means clustering algorithm with Convolutional Neural Network (CNN), where k-means is used to detect the leaf’s infected area, then CNN is used for specifying the disease. We used the PlantVillage dataset for experimental verification as it contains several crops with different kinds of challenging diseases. We also examined the selection of optimal k-value using the Silhouette coefficient, Elbow method, and Kneedle Algorithm. The Silhouette technique was analyzed using three distance metrics; Euclidean, Manhattan, and Cosine. Its scores for the three-distance metrics were low, near-zero, and failed to produce the optimum k value. Besides, the Elbow method was complicated to use in image segmentation in terms of executing and visualizing the k value in its graph plot. Based on verification results, the Kneedle Algorithm produced better results in the consistency of choosing the optimal k value and showed superiority over other approaches. Therefore, the processed images were segmented with the k-means clustering algorithm with a Kneedle algorithm-based k value. Finally, a Convolutional Neural Network (CNN) is trained to classify the type of disease based on analyzing and testing leaf images. The hybrid model achieved high accuracy of 93.79% in disease identification, confirming the proposed model’s robustness.
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
