Research Article Open Access

Early Detection and Classification Approach for Plant Diseases based on MultiScale Image Decomposition

Assia Ennouni1, Noura Ouled Sihamman1, My Abdelouahed Sabri1 and Abdellah Aarab1
  • 1 University Sidi Mohamed Ben Abdellah, Morocco

Abstract

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-L1 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%. 

Journal of Computer Science
Volume 17 No. 3, 2021, 284-295

DOI: https://doi.org/10.3844/jcssp.2021.284.295

Submitted On: 4 December 2020 Published On: 25 March 2021

How to Cite: Ennouni, A., Sihamman, N. O., Sabri, M. A. & Aarab, A. (2021). Early Detection and Classification Approach for Plant Diseases based on MultiScale Image Decomposition. Journal of Computer Science, 17(3), 284-295. https://doi.org/10.3844/jcssp.2021.284.295

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Keywords

  • Smart Agriculture
  • Image Processing
  • Machine Learning
  • Plants Disease Imaging
  • Early Diseases Detection
  • PDE
  • RelieF
  • Texture
  • Features Engineering
  • SVM