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تشخیص سه نوع بیماری برگ انگور بر پایۀ پردازش تصویر با استفاده از الگوریتم بهینهساز پروانه و ماشین بردار پشتیبان | ||
| تحقیقات سامانه‌ها و مکانیزاسیون کشاورزی | ||
| دوره 24، شماره 87، تیر 1402، صفحه 39-54 اصل مقاله (1.68 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/amsr.2024.365272.1482 | ||
| نویسندگان | ||
| محسن نجف آبادی ها1؛ داود محمدزمانی* 2؛ محمد غلامی پرشکوهی3 | ||
| 1دانشجوی دکتری، گروه مهندسی بیوسیستم، واحد تاکستان، دانشگاه آزاد اسلامی، تاکستان، ایران | ||
| 2دانشیار گروه مهندسی بیوسیستم، واحد تاکستان،دانشگاه آزاد اسلامی، تاکستان، ایران. | ||
| 3دانشیار گروه مهندسی مکانیک، واحد شهر قدس، دانشگاه آزاد اسلامی، تهران، ایران | ||
| چکیده | ||
| امروزه تکنیک‌های هوش مصنوعی و فناوری‌های یادگیری ماشین توانسته است شناسایی و طبقهبندی بیماریهای گیاهی را آسان کند. در این پژوهش، به منظور تشخیص و طبقهبندی برخی بیماریهای برگ گیاه انگور با نامهای پوسیدگی سیاه، اسکای (زوال) و لکه برگی، پس از حذف پس زمینه از تصویر برگها و استخراج ویژگیهای بافت و رنگ و شکل از تصاویر، از ترکیبی از طبقهبند ماشین بردار پشتیبان و الگوریتم بهینهساز پروانه برای انتخاب مهمترین ویژگیها در تشخیص بیماری برگ گیاه انگور استفاده شد. نتایج صحت طبقهبندی برای بیماریهای پوسیدگی سیاه، اسکای (زوال) و لکه برگی و برگ سالم به ترتیب 100، 100، 100 و 95 درصد و دقت طبقهبندی برای تشخیص کل گروههای بیمار و سالم 75/98 درصد به دست آمد. نتایج طبقهبندی نشان داد که توان پردازش تصویر و یادگیری ماشین در تشخیص و طبقهبندی برخی بیماریهای گیاهی برگ انگور عالی است. در این پژوهش همچنین 15 ویژگی بافت، رنگ و شکل به کمک الگوریتم انتخاب ویژگی بهینهساز پروانه به پژوهشگران بیماری شناسی گیاهی و علوم داده معرفی شده است. | ||
| کلیدواژه‌ها | ||
| استخراج ویژگی؛ انتخاب ویژگی؛ بیماری های انگور؛ تشخیص و طبقهبندی؛ هوش مصنوعی | ||
| عنوان مقاله [English] | ||
| Diagnosis of Three Types of Grape Leaf Diseases Based on Image Processing using Butterfly Optimization Algorithm and Support Vector Machine | ||
| نویسندگان [English] | ||
| mohsen najafabadiha1؛ Davood Mohammad Zamani2؛ 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. | ||
| چکیده [English] | ||
| 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. | ||
| کلیدواژه‌ها [English] | ||
| Artificial Intelligence, Diagnosis and Classification, Feature Extraction, Feature Selection, Grape Diseases | ||
| مراجع | ||
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