Journal of computer sciences | |
Early Detection and Classification Approach for Plant Diseases based on MultiScale Image Decomposition | |
article | |
Assia Ennouni1  Noura Ouled Sihamman1  My Abdelouahed Sabri1  Abdellah Aarab1  | |
[1] University Sidi Mohamed Ben Abdellah | |
关键词: Smart Agriculture; Image Processing; Machine Learning; Plants Disease Imaging; Early Diseases Detection; PDE; RelieF; Texture; Features Engineering; SVM; | |
DOI : 10.3844/jcssp.2021.284.295 | |
学科分类:计算机科学(综合) | |
来源: Science Publications | |
【 摘 要 】
This paper presents a new and powerful approach for detecting and classifying leaf diseases for plant diagnosis with high accuracy. The main contribution of this paper is that a hybrid approach is proposed by using the combination of Partial Differential Equations (PDE) based image decomposition, segmentation, feature extraction, features selection and classification aiming to improve the classification accuracy and provide an excellent diagnosis. The TV-L 1 Total variation model is adopted to separate the original image into texture and object components. Segmentation will be done only on the object component. Then texture, color, vein and shape features are extracted and merged in a feature vector using the codebook method. Moreover, features are selected by the RelieF feature selection algorithm to keep only relevant ones. In the classification, only selected features will be used and passed to the Multiclass Support Vector Machine algorithm SVM. The proposed approach is implemented and tested on the PV Plant Village dataset and provided a good and greater classification accuracy compared with the existing approaches from the literature. The obtained results proved that the use of PDE influences on the segmentation, which in turn, allowed us to identify correctly the leaves and provide new and optimal features, those features improves the classification accuracy rate to 95.9%.
【 授权许可】
CC BY
【 预 览 】
Files | Size | Format | View |
---|---|---|---|
RO202107250000253ZK.pdf | 670KB | download |