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شناسایی ژنوتیپ های سیب زمینی پس از دورۀ انبارمانی با استفاده از ترکیب روش های پردازش تصویر و شبکۀ عصبی مصنوعی | ||
| تحقیقات سامانهها و مکانیزاسیون کشاورزی | ||
| مقاله 10، دوره 21، شماره 74، شهریور 1399، صفحه 135-150 اصل مقاله (1.31 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/erams.2019.124819.1295 | ||
| نویسندگان | ||
| رویا فرهادی1؛ امیرحسین افکاری سیاح* 2؛ بهاره جمشیدی3؛ احمد موسی پور گرجی4 | ||
| 1دانشجوی دکتری رشته مهندسی مکانیک بیوسیستم، گروه مهندسی بیوسیستم، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی. اردبیل. ایران | ||
| 2دانشیار گروه مهندسی بیوسیستم، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران | ||
| 3دانشیار مؤسسه تحقیقات فنی و مهندسی کشاورزی، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران. | ||
| 4استادیار موسسه تحقیقات اصلاح و تهیه نهال و بذر، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران | ||
| چکیده | ||
| برای ایجاد اطمینان از عرضۀ پیوستۀ سیب زمینی به مصرفکنندگان و صنایع فرآوری این محصول، ذخیره سازی آن ضروری است. هنگام ذخیره سازی، تغییرات فیزیولوژیکی و اتلاف آب در سیب زمینی باعث می شود رنگ، شکل، اندازه و بافت آن تغییرکند. بنابراین، برای تعیین کیفیت محصول نیاز به روش های دقیق و سریع خواهد بود. در این مطالعه، از روش ماشین بینایی و شبکۀ عصبی مصنوعی برای کلاسه بندی و مدلسازی دو نمونه سیب زمینی ذخیره شده در شرایط ثابت و متغیر استفاده شد. از بین 29 پارامتر اندازه گیری شده مربوط به ویژگی های رنگ، بافت و مورفولوژیکی، سه ویژگی طول قطر بزرگ، فشردگی و مساحت (از ویژگی های مورفولوژیکی)، دو ویژگی L* و b* (از ویژگی های رنگی) و 2 ویژگی میانگین کنتراست و میانگین سطح خاکستری (از ویژگی های بافت) به عنوان پارامترهای اصلی انتخاب شدند. در میان الگوریتم های آموزشی، الگوریتم آموزشی لونبرگ-مارکوارت (LM) با کمترین میانگین مربعات خطا 0/012=RMSE و بیشترین ضریب تبیین 95/01 =R2 درصد مدلی بهینه در طبقه بندی (کلاسه بندی) دو نمونه سیب زمینی انبار شده در انبار غیرفنی و فنی شناخته شد. در انبار غیرفنی و فنی، دقت شناسایی ژنوتیپ اگریا به ترتیب 89/2 و 87/6 درصد و دقت شناسایی ژنوتیپ کلون 8-397009 بهترتیب 92/4 و 90/3 درصد به دست آمد. | ||
| کلیدواژهها | ||
| تغییرات فیزیولوژیکی؛ طبقه بندی؛ کیفیت؛ ماشین بینایی | ||
| عنوان مقاله [English] | ||
| Identify of Potato Genotypes by Combining Machine Vision Techniques and Neural Networks after the Storage Period | ||
| نویسندگان [English] | ||
| roya farhadi1؛ Amir Hossein Afkari Sayyah2؛ Bahareh Jamshidi3؛ ahmad mousapour gorji4 | ||
| 1Ph.D Student in Mechanical Biosystem Engineering, Department of Biosystems Engineering, Faculty of Agricultural and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran. | ||
| 2Associate Professor, Department of Biosystem Engineering, Faculty of Agricultural and Natural Resources, University of Mohaghegh-Ardabili, Ardabil, Iran | ||
| 3Associate Professor, Agricultural Engineering Research Institute, Agricultural Research Education and Extension Organization, Karaj, Iran | ||
| 4Assistant Professor, Seed and Plant Improvement Research Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran | ||
| چکیده [English] | ||
| Potato storage is essential to ensure the continued supplying of potatoes to consumers and the potato processing industry. During storage, physiological changes and water loss lead to changes in color, shape, size, and texture of potatoes. Therefore, there is a need for a quick and accurate method to measure the quality of the product. In this study, machine vision and neural network methods were used in classification and modeling of two stored potato samples (Agria and Clone 8-397009) under constant and variable conditions. Among 29 measured parameters relating to color, texture and morphological features of potato, some features were selected as the main parameters to monitor the chnges in product during storage period: Major Axis Length, Compactness, and area (morphological features), L* and b* (color features) and Average contrast (Ac) and Average gray level (Agl) (texture features). Among the training algorithms, Levenberg–Marquardt (LM) training algorithm with the lowest root mean square error (RMSE=0.012) and the highest coefficient of determination (R2=95.01) were considered as an optimal model for classification of two samples stored in non-technical and technical storage. The accuracy of identification of the Agria genotype was 89.2% and 87.6%, and the accuracy of the genotype Clone 8-397009 was 92.4% and 90.3%, in non-technical and technical storage respectively. | ||
| کلیدواژهها [English] | ||
| Classification, Machine Vision, Physiological Changes, Quality | ||
| مراجع | ||
|
Ahsani, M. J., Mahna, F., & Shokohi-Rostami, A. (2014). An effective method for assessing the quality of potato based machine vision. National Conference on Computer Engineering and Information Technology Management. May 29. Tehran, Iran. (in Persian)
Al-Mallahi, A., Kataoka, T., Okamoto, H., & Shibata, Y. (2010). Detection of potato tubers using an ultraviolet imaging-based machine vision system. Journal of Biosystems Engineering. 105, 257- 265.
Amiri-Parian, J., Khoshtaghaza, M. H., Kabir, E., & Minaei S. (2008). Volume estimation of potatoes using image processing. Journal of Agricultural Engineering Research, 8(4), 113-126.
Basati, Z., Rasekh, M., & Abbaspour-gilandeh, Y. (2018). Using different classification models in wheat grading utilizing visual features. International Agrophysics, 32, 1-11.
Basset, O., Buquet, B., Abouelkaram, S., Delachartre, P., & Culioli, J. (2000). Application of texture image analysis for the classification of bovine meat. Food Chemistry, 69(4): 437-445.
Blascoa, J., Aleixos, N., Go´mez-Sanchı´s, J., & Molto, E. (2009). Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features. Biosystems Engineering, 103, 137-145.
Brosnan, T., & Sun, D. (2004). Improving quality inspection of food products by computer vision-a review. Journal of Food Engineerig, 61, 125–135.
Connor, C. J., Fisk, K. J., Smith, B. J., & Melton L. D. (2001). Fat uptake in french fries as affected by different potato varieties and processing. Journal of Food Science, 66(6), 903-908.
Dacal-Nieto, A., Vázquez-Fernández, E., Formella, A., Martin, F., & Torres-Guijarro, S. (2009). A genetic algorithm approach for feature selection in potatoes classification by computer vision. Industrial Electronics, 35th Annual Conference of Industrial Electronics. Nov. 3-5. Portugal. 48, 1955–1960.
Dowlati, M., Guardia M., & Mohtasebi S. S. (2012). Application of machine-vision techniques to fish-quality assessment. Trac Trends in Analytical Chemistry, 40, 168-179.
Du, C. J., & Sun, D. W. (2004). Recent developments in the applications of image processing techniques for food quality evaluation. Trends in Food Science and Technology, 15, 230-249.
Du, C. J., & Sun, D. W. (2005). Comparison of three methods for classification of pizza topping using different colour space transformations.Journal of Food Engineering, 68(3), 277-287.
ElMasry, G., Cubero, S., Moltó, E., & Blasco, J. (2012). In-line sorting of irregular potatoes by using automated computer-based machine vision system. Journal of Food Engineering, 112, 60-68.
Gomez-Galindo, F., Sjöholm, I., Rasmusson, A., Widell, S., & Kaack, K. (2007). plant stress physiology: opportunities and challenges for the food industry. Critical Reviews in Food Science &Nutrition,47, 749-763.
Gonzalez, R. C., Woods, R. E., & Eddins, S.L. (2004). Digital Image Processing Using MATLAB. Prentice-Hall Press, USA.
Hafezi, N., Sheikhe-Davoodi, M. J., & Sajjadieh, S. M. (2016). A study of drying rate of sliced potatoes during radiation-vacuum drying process using regression and Artificial Neural Network models. Journal of Biosystem Engineering, 47(2), 279-289. (in Persian)
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man andCybernetics SMC, 3(6), 610-621.
Huang, Z. K., Hou, L. Y., & Li, Z. H. (2013). Image clustering using graph cuts in LAB color space. International Journal of Digital Content Technology and its Applications, 7(12), 1-7.
Izadi, H., Kamgar, S., & Raoufat, M. H. (2014). Tomato grading system using machine vision technology and neuro-fuzzy networks (ANFIS). Journal of Agricultural Machinery, 6(1): 49-59. (in Persian)
Jafarlou, M., & Farrokhi Teimourlou, R. (2014). Estimation of apple volume and its shape indentation using image processing technique and neural network. Journal of Agricultural Machinery, 4(1), 57-64. (in Persian)
Kohzadi, N., Boyd, M., Kaastra, I., Kermanshahi, B., & Scuse, D. (1995). Neural networks for forecasting: An introduction. Canadian Journal of Agricultural Economics, 43, 463-474.
Li, J., Tan, J., Martz, F. A., & Heymann, H. (1999). Image textural features as indicators of beef tenderness. Meat Science, 53(1): 17-22.
Mendoza, F., & Aguilera, J. M. (2004). Application of image analysis for classification of ripening bananas. Journal of Food Science, 69, 471-477.
Mendoza, F., Dejmek, P., & Aguilera, J. M. (2007). Colour and image texture analysis in classification of commercial potato chips. Food Research International, 40, 1146-1154.
Omid, M., Mahmoudi, A., & Omid M. H. (2010). Development of pistachio sorting system using Principal Component Analysis (PCA) assisted Artificial Neural Network (ANN) of impact acoustics. Expert Systemswith Applications, 37, 7205-7212.
Pedreschi, F. (2012). Frying of potatoes: physical, chemical, and microstructural changes. Drying Technology, 30, 707-725.
Pedreschi, F., Mery, D., Mendoza, F., & Aguilera, J. M. (2004). Classification of potato chips using pattern recognition. Journal ofFood Science, 69(6), 264-270.
Pringle, B., Bishop, C., & Clayton, R. (2009). Potatoes Postharvest. First Edition. CABI, UK.
Rady, A. M., & Guyer D. E. (2015). Rapid and/or nondestructive quality evaluation methods for potatoes: A review. Computer and Electronic in Agriculture, 117, 31-48.
Razmjooy, N., Mousavi, B. S., & Soleymani, F. (2012). A real-time mathematical computer method for potato inspection using machine vision. Journal of Computers and Mathematics with Applications, 63(1), 268-279.
Shouche, S. P., Rastogi, R., Bhagwat, S. G., & Sainis J. K. (2001). Shape analysis of grains of Indian wheat varieties. Computers and Electronics in Agriculture, 33, 55-76.
Sola, J., & Sevilla, J. (1997). Importance of data normalization for the application of neural networks to complex industrial problems. IEEE Transactions on Nuclear Science, 44(3), 1464-1468.
Spychalla, J. P., & Desborough, S. L. (1990). Fatty acids, membrane permeability, and sugars of stored potato tubers. Plant Physiology, 94, 1207-1213.
Su, Q., Kondo, N., Li, M., Sun, H., & Al Riza, F. D. (2017). Potato feature prediction based on machine vision and 3D model rebuilding. Computers and Electronics in Agriculture, 137, 41-51.
Tavakoli, M., & Najafzadeh, M. (2015). Application of the image processing technique for separating sprouted potatoes in the sorting line. Journal of Applied Environmental and Biological Sciences, 4(11S), 223-227.
Teimouri, N., Omid, M., Mollazade, K., & Rajabipour, A. (2016). Separation of touching almonds and their quality classification by combining image processing and artificial neural networks techniques. Journal of Biosystem Engineering, 46(4), 355-362. (in Persian)
Yazdanpanah-Gangachin, M., & Ziaiifar. A. M. (2014). Evaluation of potato chips color using image processing. Journal of Food Research. 24(2), 239-247. (in Persian)
Zhao-Yan, L., & Fang, C. (2005). Identification of rice seed varieties using neural network. Journal of Zhejiang University Science. 6B(11), 1095-1100.
Zhou, L., Chalana, V., & Kim, Y. (1998). PC-based machine vision system for real-time computer-aided potato inspection. Image Computing Systems Laboratory, Department of Electrical Engineering, 9(6), 423-433. | ||
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