Diagnosis and Classification of Two Common Potato Leaf Diseases (Early Blight and Late Blight) Using Image Processing and Machine Learning

ماشین های کشاورزی

Volume 15, Issue 1 - Serial Number 35, March 2025, Pages 65-79   

Document Type: Research Article-en

DOI: 10.22067/jam.2024.86571.1226

Authors

H. Koroshi Talab¹; D. Mohammad Zamani^{* 1}; M. Gholami Parashkoohi²

¹Department of Biosystems Engineering, Takestan Branch, Islamic Azad University, Takestan, Iran

²Department of Mechanical Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran

Abstract

Diagnosing plant diseases is an important part of crop management and can significantly affect the quantity and quality of production. Traditional methods of visual assessment by human observers are time-consuming, costly, and error-prone, making accurate diagnosis and differentiation between various diseases difficult. Advances in agriculture have enabled the use of non-destructive machine vision systems for plant disease detection, and color imaging sensors have demonstrated great potential in this field. This study presents a framework for diagnosing early blight and late blight diseases in potatoes using a combination of Relief feature selection algorithms and Random Forest classification, along with color, texture, and shape features in three color spaces: RGB, HSV, and CIELAB (Lab*). The results indicated that the diagnostic accuracy for the early blight and late blight disease groups, as well as the healthy leaf group, were 94.71%, 95%, and 95.2%, respectively. The overall accuracy for disease classification was 95.99%. Additionally, the diagnostic accuracy for the early blight and late blight disease groups, along with the healthy leaf group, was 91.07%, 98.36%, and 98.93%, respectively, with an overall classification accuracy of 96.12%. After separating the diseased area from the healthy part of the leaf, a total of 150 features were extracted, including 45 color, 99 textural, and 6 shape features. The most effective features for disease detection were identified using a combination of all three feature sets. This study demonstrated that integrating these three sets of features can lead to a more accurate classification of potato leaves and provide valuable insights into the diagnosis and classification of potato diseases. This approach can help farmers and other plant disease specialists to accurately diagnose and manage potato diseases, and ultimately lead to an increase in product quality and yield.

Keywords

Artificial intelligence; Disease diagnosis; Feature extraction; Feature selection; Potato diseases

Supplementary Files

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