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پردازش تصاویر فراطیفی به منظور تشخیص آلودگی مغز پسته به دو جدایه KK11 و R5 قارچ Aspergillus flavus | ||
| مهندسی بیوسیستم ایران | ||
| دوره 52، شماره 1، اردیبهشت 1400، صفحه 13-25 اصل مقاله (855.85 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijbse.2020.299712.665293 | ||
| نویسندگان | ||
| کامران خیرعلی پور* 1؛ حجت احمدی2؛ علی رجبی‌پور3؛ شاهین رفیعی3؛ محمد جوان نیکخواه4؛ دیگویر جایاس5؛ کالیرامش سیلیوریو6؛ علی ملیحی پور7 | ||
| 1عضو هیئت علمی/گروه مهندسی مکانیک بیوسیستم، دانشکده کشاورزی، دانشگاه ایلام، ایلام، ایران. | ||
| 2عضو هیات علمی/گروه ماشین های کشاورزی، دانشکده مهندسی و فناوری کشاورزی، دانشگاه تهران، کرج، ایران | ||
| 3عضو هیات علمی/گروه ماشین های کشاورزی، دانشکده مهندسی و فناوری کشاورزی، دانشگاه تهران، کرج، ایران. | ||
| 4عضو هیات علمی/گروه گیاه پزشکی، پردیس کشاورزی و منابع طبیعی دانشگاه تهران، کرج، ایران. | ||
| 5عضو هیات علمی/گروه زیست سامانه ها، دانشکده مهندسی، دانشگاه منی توبا، وینیپگ، کانادا | ||
| 6گروه علوم و صنایع غذایی، دانشکده کشاورزی، دانشگاه ایالتی کانزاس، منهتن، آمریکا. | ||
| 7استادیار، بخش تحقیقات غلات، مؤسسه تحقیقات اصلاح و تهیه نهال و بذر، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، استان البرز، ایران | ||
| چکیده | ||
| فناوری تصویربرداری فراطیفی به عنوان روشی نوین و موثر در تشخیص آلودگی محصولات کشاورزی به کار می‌رود. این روش برای تشخیص پسته سالم و آلوده به قارچ Aspergillus flavus با و بدون در نظر گرفتن مراحل آلودگی، مورد استفاده قرار گرفت. جدایه‌های R5 وKK11 به ترتیب با و بدون قابلیت تولید سم آفلاتوکسین، به طور جداگانه، برای آلوده‌سازی پسته استفاده شد. از بین طول موج‌های 960 تا 1700 نانومتر، سه طول موج موثر 1090، 1280، و 1700 نانومتر با استفاده از روش تحلیل مولفه‌های اصلی انتخاب شد. پس از استخراج ویژگی، از روشهای اعتبارسنجی K-بخشی، ماشین بردار پشتیبان، و شبکه عصبی مصنوعی برای طبقهبندی استفاده شد. نتایج نشان داد که دقت روش اعتبارسنجی K-بخشی در طبقهبندی نمونه‌های پسته سالم و آلوده بدون در نظر گرفتن مراحل آلودگی و نوع جدایه (71/99 درصد) بالاتر بود. حداکثر دقت طبقهبندی الگوریتم‌های توسعه یافته در تشخیص نوع جدایه و مراحل آلودگی 69 تا 91 درصد به دست آمد. | ||
| کلیدواژه‌ها | ||
| پسته؛ آلودگی قارچی؛ مرحله آلودگی؛ فناوری تصویربرداری فراطیفی؛ پردازش تصویر | ||
| عنوان مقاله [English] | ||
| Processing the Hyperspectral Images for Detecting Infection of Pistachio Kernel by R5 and KK11 Isolates of Aspergillus flavus Fungus | ||
| نویسندگان [English] | ||
| Kamran Kheiralipour1؛ Hojjat Ahmadi2؛ Ali Rajabipour3؛ Shahin Rafiee3؛ Mohammad Javan Nikkhah4؛ Jayas Digvir5؛ Kaliramesh Siliveru6؛ Ali Malihipour7 | ||
| 1Faculty member/ Mechanical Engineering of Biosystems Department, Faculty of Agriculture, Ilam University, Ilam, Iran. | ||
| 2Faculty member/Department of Agricultural, Faculty of agriculture and Natural Resources, University of Tehran, Karaj, Iran. | ||
| 3Faculty member/Department of Agricultural, Faculty of agriculture and Natural Resources, University of Tehran, Karaj, Iran. | ||
| 4Faculty member/Department of Plant protection, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. | ||
| 5Faculty member/Department of Biosystems, Faculty of Engineering, University of Manitoba, Winnipeg, Canada. | ||
| 6Faculty member/ Department of Food Science and Industry, Faculty of Agriculture , Kansas State University, Manhattan, USA> | ||
| 7Assistant Professor, Cereal Research Department, Seed and Plant Improvement Institute (SPII), AREEO, Karaj, Alborz Province, Iran | ||
| چکیده [English] | ||
| Hyperspectral imaging technique as a new and efficient method is applied for detecting infection in agricultural products. It was used for classification of healthy and infected pistachio kernels by Aspergillus flavus fungus with and without considering infection stages. Two different fungus isolates, R5 and KK11 with and without capable of producing aflatoxin, respectively, were individually used to infect the pistachio kernel samples. From 960 to 1700 nm, three effective wavelengths of 1090, 1280, and 1700 nm were selected by principle component analysis method. After feature extraction, K-fold cross validation, support vector machine, and artificial neural network methods were used for classification. The results showed that the classification accuracy of the K-fold cross validation method was higher for classifying the healthy and infected pistachios without considering the infection stages and isolate type (99.71%). The maximum accuracy of the developed algorithms in classification of isolate type and infection stage was obtained as 69-91%. | ||
| کلیدواژه‌ها [English] | ||
| Pistachio, Fungal infection, Infection stage, Hyperspectral imaging technique, Image processing | ||
| مراجع | ||
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