Image Processing Based Method for Automatic Detection of Grape leaf Diseases

Document Type : Research Paper

Authors

1 Department of Computer Engineering, Faculty of Engineering, University of Bonab, Bonab,, Iran

2 Mechanical Engineering Department, Faculty of Engineering, University of Bonab, Bonab, Iran

Abstract

 Rapid detection and prevention of disease spread in agricultural products can significantly reduce losses and costs of disease control. In this study, an intelligent system based on image processing method has been presented for detection of grape (Sultana - Vitis vinifera) leaf diseases. For this purpose, different image texture features were extracted from the Gray Level histogram (GLH), Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRM) and Local Binary Pattern (LBP) algorithms. Two models of Artificial Neural Network (ANN) and Support Vector Machine (SVM) were used to model the features. The dataset consists of 4062 images including healthy leaves, Black Rot, Esca and Isariopsis leaves. The results showed that the SVM model based on GLRM features with an average accuracy of 89.70% showed the best performance. The results also showed that the use of all extracted features as a single feature vector increases the accuracy of classification. The accuracy of the SVM and ANN models using all of the features for training data were 91.10%, 95.04%, and for the test data were 89.93% and 91.75%, respectively. Finally, using Genetic Bee Colony (GBC) algorithm and reducing the number of features to 34 and 46 for ANN and SVM models, respectively, the average accuracy of 97.20% and 94.10% for training and testing of ANN model and 93.01% and 92.33% for training and testing of SVM model were obtained, which shows the improvement of results by GBC algorithm. The proposed method was evaluated as efficient in diagnosing grape leaf diseases.

Keywords

References

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Iranian Journal of Biosystems Engineering
Volume 53, Issue 1
April 2022
Pages 61-76

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History

  • Receive Date: 15 October 2021
  • Revise Date: 04 February 2022
  • Accept Date: 08 February 2022

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