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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 ارشد 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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Abidi, A., Heydaran Daroogheh Amnyieh, Z., Jamahmoodi, H., Salarniya, S., & Zabbah, I. (2023). Improving the diagnosis of arrhythmia using a combination of neural networks in a hierarchical way. Journal of Health and Biomedical Informatics, 10(3), 223-237. (in Persian) Aghaei, M., & Rezagholizadeh, M. (2011). Iran's comparative advantage in production of Saffron. Journal of Agricultural Economics and Development, 25(1), 121-132. https://doi: 10.22067/jead2.v1390i1.8897. Aliabadi, R. (2013). Using smart techniques to check the quality of saffron flower. Kerman Shahid Bahonar University. Kerman .(in Persian) AliAbadi, R., & Mohammadi, M. (2012). Presentation of a new method for saffron flower cutting automation using intelligent techniques. Proceedings of the 2nd National Conference on Computer Engineering, Electrical and Information Technology. May 24. Khomein Islamic Azad University. Iran. (in Persian) Aliakbari, P., Salari, A., & KhasheiSiuki, A. (2018). Determine of the actual and potential evapotranspiration and appropriate model for determining water requirement of saffron (Case study: Torbat Heydarieh). Iranian journal of Ecohydrology, 5(3), 1051-1061. (in Persian) Beiki, A. H. (2014). Classification and prediction of three and multi stigma in saffron bystatistical, unsupervised machine learning tools. Saffron Agronomy and Technology, 2(3), 199-204. (in Persian) Birjandi Toroghi, Z., Moradinezhad, F., Niazmand, R., & Bayat, H. (2023). Effect of vacuum and active packaging on the qualitative characteristics and microbial load of dry saffron stigma (Crocus sativus L.). Saffron Agronomy and Technology, (in Press). doi: 10.22048/jsat.2023.397103.1487. (in Persian) Caiola, M. G., & Canini, A. (2010). Looking for saffron’s (Crocus sativus L.) parents. Functional Plant Science and Biotechnology, 4(2), 1-14. Colak, M. C., Çolak, C., Koçatürk, H., Sağıroğlu, Ş., & Barutçu, I. (2008). Predicting coronary artery disease using different artificial neural network models. Anadolu Kardiyol Derg, 8(4), 249-54.
Dehbashi, M., Rajaei, A., & Kardan Moghadam, H. (2022). Locating and recognizing of saffron flowers using image processing. Saffron Agronomy and Technology, 10(3), 227-260. doi: 10.22048/jsat.2022.290185.1427. Jafarbeyglu, M., & Mobaraky, Z. (2008). The land proportion evaluation in Ghazvin province for Saffron cultivation based on Multi Criteria Decision method. Natural Geographic Research, 66, 101-119. (in Persian) Karbasi, A., Hosseini, M., Kareshki, H., & Moghimi, Z. (2020). Evaluation of awareness, attitude and willing of Saffron farmers to application of saffron research. Journal of Saffron Research, 8(2), 207-221. doi: 10.22077/jsr.2020.2612.1105. Khedri, A., Moradinejad, T., Dashti Barmaki, M., & Eskandari, M. (2024). Zoning of drinking water quality using entropy method and its relationship with the drought (Case study: Abdalan Aquifer, Gachsaran). Journal of Drought and Climate change Research, 1(4), 17-36. doi: 10.22077/jdcr.2023.6366.1023. (in Persian) Lotfizadeh, A. (1965). Fuzzy sets. Information and Control. 8, 338-353. Moghaddasi, M. S. (2010). Saffron chemicals and medicine usage. Journal of Medicinal Plants Research, 4(6), 427-430. Mohamadzadeh Moghadam, M., Taghizadeh, M., Sadrnia, H., & Pourreza, H. R. (2020).Classification of saffron using color features extracted from the image. Saffron Agronomy and Technology, 8(3), 319-399. (in Persian) Nekouei, N., Behdani, M. A., & KhasheiSiuki, A. (2017). Predicting saffron yield from meteorological data using expert system, razavi and South Khorasan provinces. Journal of Saffron Research, 2(1), 15-33. (in Persian) Qurani, B., Kadekhodai, R., & Al-Hosseini., A. (2017). The effect of biopolymer type, temperature and relative humidity on the physicochemical characteristics and stability of microencapsulated bioactive compounds of saffron. Journal of Food Science and Technology (Iran), 14 (64), 127-142. (in Persian) Rashid Sorkhabadi, M., Shahidi, A., & Khashei, S. A. (2014). Determination of suitable region for saffron cultivation based on water and soil characteristics using hierarchical analysis process method (Case Study: Torbate Hydariyeh City). Journal of Saffron Research. 2(1) , 58-72. doi:10.22077/jsr.2015.330. (in Perian) Rezvani Moghaddam, P., Khorramdel, S., & Moalem Benhangi, F. (2022). Optimization of manure and irrigation levels on flower and corm yields of saffron by using a central composite design. Journal of Saffron Research, 10(1), 45-63. https://doi: 10.22077/jsr.2021.4086.1152. (in Persian) Riahi Modavar, H., KhasheiSiuki, A., & Seifi, A. (2017). Accuracy and uncertainty analysis of artificial neural network in predicting saffron yield in the South Khorasan province based on meteorological data. Saffron Agronomy & Technology, 5(3), 255-271. (in Persian) Sadeghiravesh, M. H., Zehtabian, G. R., & Tahmores, M. (2012). Vulnerability assessment of environmental issues to desertification risk, Case study: Khezrabad region, Yazd. Watershed Management Research, 96, 75-87. (in Persian) Saeidirad, M. H., & Zarifneshat, S. (2021). Development and performance evaluation of an automatic saffron corm planter. Iranian Journal of Biosystems Engineering, 51(4), 683-693. Sanaeinejad, S. H., Salajegheh, M., Hosseini, S. N., & Araghizadeh, M. (2010). The effects of weather on saffron yield in southern Khorasan province by experimental method. Proceedings of the First International Conference on Plant, Water, Soil and Weather Modeling. 14-15 Nov, Kerman, Iran. (in Persian) Taheri, T., Mousavi, S. M., Vakili, S. M. B., & Taheri, H. (2012). Flux modeling in separation of anthocyanin membrane from saffron petals using fuzzy logic. Proceedings of the Third Conference on Science and Engineering of Separation. May 2-4. Zahedan, Iran. (in Persian) Yasrebi, S. E., Zabbah, I., Behzadiyan, B., Maroosi, A., & Rezaie, R. (2019). Classification of saffron based on its apparent characteristics using artificial neural networks. Saffron Agronomy and Technology, 7(4), 521-535. http://doi: 10.22048/jsat.2019.149440.1316. (in Persian) Younesi, M., & Goodarzi, M. (2016). Comparison of Fuzzy Neural Network Forecasting Potential (ANFIS) with Neural Network Models (ANN) and ARIMA Automatinising in estimating export prices of agricultural products (Case study: monthly price of saffron). Proceedings of the Fourth International Conference on Science and Engineering. Sep. 19, Rome, Italy. Zarghani, F., Karimi, A., Khorasani, R., & Lakzian, A. (2016). To evaluation the effect of soil physical and chemical characteristics on the growth characteristics of saffron (Crocus Sativus L.) corms in Torbat-E Heydariyeh area. Journal of Agroecology, 8(1), 120-33. (in Persian) | ||||
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