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Diagnosis of Three Types of Grape Leaf Diseases Based on Image Processing using Butterfly Optimization Algorithm and Support Vector Machine | ||
| تحقیقات سامانهها و مکانیزاسیون کشاورزی | ||
| Volume 24, Issue 87, June 2024, Pages 39-54 PDF (1.68 M) | ||
| Document Type: Original Article | ||
| DOI: 10.22092/amsr.2024.365272.1482 | ||
| Authors | ||
| mohsen najafabadiha1; Davood Mohammad Zamani* 2; mohammad Gholami Parashkoohi3 | ||
| 1PhD student, Department of Biosystems Engineering, Takestan Branch, Islamic Azad University, Takestan, Iran | ||
| 2Associate Professor, Department of Biosystems Engineering,Takestan Branch, Islamic Azad University, Takestan, Iran | ||
| 3Associate Professor, Department of Mechanical Engineering, Shahr-e-Qods Baranch, Islamic Azad University, Tehran, Iran. | ||
| Abstract | ||
| Today, artificial intelligence techniques and machine learning technologies have made it easy to identify and classify plant diseases. In this research, in order to diagnose and classify some diseases of grapevine leaves with the names of black measles, black rot, and leaf blight, after removing the background from the image of the leaves and extracting the characteristics of texture and color and from the images, a combination of support vector machine classification and butterfly optimization algorithm was used to select the most important features in the diagnosis of grape plant leaf disease. The results of classification accuracy for black measles, black rot, leaf blight, and healthy leaf diseases are 100, 100, 100 and 95% respectively, and the classification accuracy for the diagnosis of all diseased and healthy groups is 98.75%. It was achieved. The classification results showed that image processing and machine learning are excellent in diagnosing and classifying some plant diseases of grape leaves. In this research, 15 features of texture, color and shape have been introduced to the researchers of plant pathology and data science with the help of the butterfly optimization feature selection algorithm. | ||
| Keywords | ||
| Artificial Intelligence; Diagnosis and Classification; Feature Extraction; Feature Selection; Grape Diseases | ||
| References | ||
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