روش مبتنی بر پردازش تصویر به منظور تشخیص خودکار بیماری برگ درخت انگور

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه بناب، بناب، ایران

2 گروه مهندسی مکانیک، دانشکده فنی و مهندسی، دانشگاه بناب، بناب، ایران

چکیده

تشخیص سریع و پیشگیری از گسترش بیماری­ محصولات کشاورزی، می­تواند تلفات مقابله با بیماری را به میزان قابل توجهی کاهش دهد. در این پژوهش، سامانه­ای هوشمند بر مبنای پردازش تصویر برای تشخیص بیماری­های برگ درخت انگور (Sultana - Vitis vinifera) ارائه گردیده است. بدین منظور، ویژگی­های مختلف بافت تصویر از هیستوگرام سطح خاکستری (GLH)، ماتریس هم­-رخداد سطح خاکستری (GLCM)، ماتریس طول بردار سطح خاکستری (GLRM) و الگوی دودویی محلی (LBP) استخراج شد. برای مدل­سازی ویژگی­ها، از دو مدل شبکه عصبی (ANN) و ماشین بردار پشتیبان (SVM) استفاده شد. پایگاه داده­ مورد استفاده، متشکل از 4062 تصویر، شامل برگ سالم، مبتلا به پوسیدگی سیاه، اسکا و لکه ایزاریوپسیس است. نتایج نشان دادند که مدل SVM با استفاده از ویژگی­های GLRM با متوسط دقت 70/89% بهترین عملکرد را از خود نشان داد. همچنین نتایج نشان دادند، استفاده از تمام ویژگی­های استخراج یافته به ‌صورت بردار ویژگی واحد، افزایش دقت دسته­بندی را به دنبال دارد. مدل SVM و ANN با استفاده از تمام ویژگی­ها بترتیب برای داده­های آموزشی دقت 10/91%، 04/95 % و برای داده­های آزمون میزان دقت 93/89% و 75/91% را نتیجه دادند. در نهایت، با استفاده از الگوریتم کلونی زنبور ژنتیکی (GBC) و کاهش تعداد ویژگی­ها به 34 و 46 به ترتیب برای مدل­های ANN و  SVM میانگین دقت 20/97% و 10/94% برای آموزش و آزمون مدل ANN و 01/93% و 33/92% برای آموزش و آزمون مدل SVM به دست آمد که نشان دهنده بهبود نتایج توسط الگوریتم GBC می­باشد. روش پیشنهادی در تشخیص بیماری­های برگ انگور کارآمد ارزیابی شد.

کلیدواژه‌ها


عنوان مقاله [English]

Image Processing Based Method for Automatic Detection of Grape leaf Diseases

نویسندگان [English]

  • Sajjad Nasiri 1
  • Mostafa Khojastehnazhand 2
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
چکیده [English]

 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.

کلیدواژه‌ها [English]

  • Machine learning
  • Image texture analysis
  • Black rot
  • Esca
  • Isariopsis
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