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ارزیابی فازی ویژگی های ظاهری زعفران با استفاده از روشهای مبتنی بر هوش مصنوعی | ||||
| تحقیقات سامانهها و مکانیزاسیون کشاورزی | ||||
| دوره 24، شماره 85، اردیبهشت 1402، صفحه 75-90 اصل مقاله (1.79 M) | ||||
| نوع مقاله: مقاله پژوهشی | ||||
| شناسه دیجیتال (DOI): 10.22092/amsr.2024.364271.1471 | ||||
| نویسندگان | ||||
| ایمان ذباح* 1؛ زهرا حیدران داروقه امنیه2؛ بهناز بهزادیان3؛ رویا رضایی4 | ||||
| 1گروه کامپیوتر، واحد تربتحیدریه، دانشگاه آزاد اسلامی، تربتحیدریه، ایران؛ و پژوهشگر پژوهشکده زعفران، دانشگاه تربت حیدریه، تربتحیدریه، ایران | ||||
| 2گروه برق، واحد دولتآباد، دانشگاه آزاد اسلامی، اصفهان، ایران | ||||
| 3دانشآموخته کارشناسی گروه برق و کامپیوتر، دانشگاه تربت حیدریه، تربت حیدریه، ایران | ||||
| 4گروه علوم و صنایع غذایی، واحد تربتحیدریه، دانشگاه آزاد اسلامی تربتحیدریه، ایران؛ و پژوهشگر پژوهشکده زعفران، دانشگاه تربت حیدریه، تربتحیدریه، ایران | ||||
| چکیده | ||||
| ایران بزرگترین تولیدکنندۀ زعفران در جهان است. زعفران دارای اهمیت بالای اقتصادی است و به دلیل افزایش فعالیت رقبای صادراتی، نیازمند حمایت و گسترش و توسعۀ صادرات است. روند رو به رشد سهم صادرات زعفران از یکسو و درآمدزایی و کارآفرینی برای روستاییان کشور از سوی دیگر، لزوم توجه به این محصول را دوچندان میکند. رقابت در این عرضه و با وجود رقبای قدرتمند در گرو مکانیزه کردن فرایندهای کاشت تا برداشت و درجهبندی تا بستهبندی امکانپذیر خواهد بود. بنابراین، ارزیابی ویژگیهای ظاهری زعفران به منظور درجهبندی صحیح نیاز اساسی محسوب میشود. در پژوهش حاضر، ویژگیهای ظاهری با هدف درجهبندی زعفران با استفاده از روشهای مختلف هوش مصنوعی شامل شبکههای عصبی مصنوعی پرسپتورن چند لایه (MLP)، (LVQ)، شبکه عصبی غیر نظارت شده (SOM)، سیستمهای فازی (FNN) و سیستم استنتاج فازی عصبی (ANFISS) بررسی شده است. پایگاه داده مورد مطالعه مربوط به 113 نمونه زعفران است که در 4 طبقه زعفران پوشال (نگین)، پوشال درجه 2 (خوب)، پوشال درجه سه (معمولی) و پوشال درجه چهار (ضعیف) که محققان این پژوهش آنها را گردآوری کردهاند. نتایج تحلیل نشان میدهد که درجهبندی زعفران با در نظر گرفتن شرایط عدم قطعیت در مدل ANFIS نسبت به دیگر روشها بهتر است، به طوری که دقت طبقهبندی 5/97 درصد و میزان خطای 3484/0 در تشخیص نمونههای آزمون به دست آمد. | ||||
| کلیدواژهها | ||||
| درجهبندی زعفران؛ سیستم استنتاج فازی عصبی؛ شبکۀ عصبی مصنوعی | ||||
| عنوان مقاله [English] | ||||
| Fuzzy evaluation of the appearance characteristics of saffron using methods based on artificial intelligence | ||||
| نویسندگان [English] | ||||
| iman zabbah1؛ zahra heydaran2؛ behnaz behzadiyan3؛ roya Rezaie4 | ||||
| 1Department of Computer, Torbat Heydariyeh Branch, Islamic Azad University, Torbat Heydarieh, Iran; Saffron Institute, University of Torbat Heydarieh, Torbat Heydarieh, Iran. | ||||
| 2Assistant Professsor Department of Electronic Engineering,Dolatabad Branch, Islamic Azad University, Isfahan,Iran | ||||
| 33. Bachelor of Computer University of Torbat Heydarieh, Torbat Heydarieh,Iran | ||||
| 4, Department of Food Science and Technology, Islamic Azad University of Torbat Heydarieh, and Director of R & D ,Department, Saffron Kian Toos Co, Torbat Heydarieh,Iran | ||||
| چکیده [English] | ||||
| Iran is the largest saffron producer in the world. Saffron is of great economic importance and due to increasing activities of export competitors, it requires support, expansion, and development of exports. The growing trend of saffron export share on one hand and income generation and entrepreneurship for rural residents on the other hand double the necessity of attention to this product. Competition in this supply chain, despite powerful competitors, will be possible through mechanizing processes from cultivation to harvesting and grading to packaging. Therefore, evaluating the physical characteristics of saffron for proper grading is considered essential. In this study, the physical characteristics for the purpose of saffron grading using various artificial intelligence methods including Multilayer Perceptron Neural Networks (MLP), Learning Vector Quantization (LVQ), Self-Organizing Maps (SOM), Fuzzy Neural Networks (FNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) have been investigated. The database under study relates to 113 saffron samples categorized into 4 classes of Saffron Negin (top quality), Grade 2 Saffron (good), Grade 3 Saffron (normal), and Grade 4 Saffron (poor) collected by the researchers. The analysis results show that saffron grading considering uncertainties in the ANFIS model is superior to other methods, with a classification accuracy of 97.5% and a test sample detection error rate of 0.3484. | ||||
| کلیدواژهها [English] | ||||
| Artificial Neural Network, Neuro-Fuzzy Inference System (ANFIS), Saffron Evaluation, Saffron Grading | ||||
| مراجع | ||||
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