Acoustic detection of apple mealiness based on support vector machine

Document Type: Full Article


Department of Biosystems Engineering, College of Agriculture, Arak University, Arak, I. R. Iran



Mealiness degrades the quality of apples and plays an important role in fruit market. Therefore, the use of reliable and rapid sensing techniques for nondestructive measurement and sorting of fruits is necessary. In this study, the potential of acoustic signals of rolling apples on an inclined plate as a new technique for nondestructive detection of Red Delicious apple mealiness was investigated. According to destructive confined compression tests, the mealiness of apples was evaluated by the hardness and juiciness measurements. In addition, support vector machine (SVM) models were developed to classify apples. The radial basis function (RBF) as the kernel was used in SVM models. According to exhaustive search method, the model with nine features combination was found to be the best model. Results indicated overall accuracy of 85.5 % to classify apples in mealy and healthy groups. The results indicated that this method is potentially useful for apple mealiness detection.


Article Title [Persian]

تشخیص صوتی آردی شدن سیب براساس ماشین‌بردار پشتیبان

Authors [Persian]

  • مجید لشگری
  • عبداله ایمان مهر
بخش مکانیک بیوسیستم، دانشکده کشاورزی و منابع طبیعی، دانشگاه اراک، اراک، ج. ا. ایران
Abstract [Persian]

آردی‏شدن، کیفیت میوه سیب را تنزل می‏دهد و این پدیده نقش مهمی در بازار میوه ایفا می‏کند. بنابراین استفاده از تکنیکی سریع و قابل اعتماد برای اندازه‏گیری و درجه‏بندی میوه‏ها ضروری است. در این تحقیق، قابلیت سیگنال‏های صوتی سیب‏های غلتان بر روی صفحه شیب‏دار به عنوان روشی نوین در تشخیص غیرمخرب آردی شدن سیب رقم رد دلیشز مورد بررسی قرار گرفت. با استفاده از آزمون مخرب فشردگی محصور، میزان آردی شدن نمونه‏ها ارزیابی شد. مدل‏های ماشین بردار پشتیبان برای طبقه‏بندی سیب‏ها در نظر گرفته شد. از کرنل تابع پایه شعاعی در مدل‏های ماشین بردار پشتیبان استفاده شد. مطابق روش جستجوی جامع، مدلی با ترکیب 9 ویژگی به عنوان بهترین مدل انتخاب شد. نتایج نشان داد که میزان دقت کلی این روش برای تشخیص سیب‏های سالم و آردی برابر 5/85 درصد به دست آمد. نتایج حاکی از آن بود که روش مذکور از توانمندی خوبی برای تشخیص سیب‏های آردی برخوردار است

Keywords [Persian]

  • طبقه‏بندی
  • صفحه شیب‏دار
  • رقم رد دلیشز
  • کرنل تابع پایه شعاعی
Arana, I., Jarén, C., & Arazuri, S. (2004). Apple mealiness detection by non-destructive mechanical impact. Journal of Food Engineering, 62(4), 399-408.
Arefi, A., Moghaddam, P. A., Mollazade, K., Hassanpour, A., Valero, C., & Gowen, A. (2015). Mealiness detection in agricultural crops: destructive and nondestructive tests: A review. Comprehensive Reviews in Food Science and Food Safety, 14(5), pp.657-680.
 Arefi, A., Moghaddam, P. A., Hassanpour, A., Mollazade, K., & Motlagh, A. M. (2016). Non-destructive identification of mealy apples using biospeckle imaging. Postharvest Biology and Technology, 112, 266-276.
Bechar, A., Mizrach, A., Barreiro, P., & Landahl, S. (2005). Determination of mealiness in apples using ultrasonic measurements. Biosystems Engineering, 91(3), 329-334.
Chen, F. L., & Li, F. C. (2010). Combination of feature selection approaches with SVM in credit scoring. Expert Systems with Applications, 37(7), 4902-4909.
Corollaro, M. L., Aprea, E., Endrizzi, I., Betta, E., Demattè, M. L., Charles, M., Bergamaschi, M., Costa, F., Biasioli, F., Grappadelli, L. C., & Gasperi, F. (2014). A combined sensory-instrumental tool for apple quality evaluation. Postharvest Biology and Technology 96, 135–144.
Diezma-Iglesias, B., Valero, C., García-Ramos, F. J., & Ruiz-Altisent, M. (2006). Monitoring of firmness evolution of peaches during storage by combining acoustic and impact methods. Journal of Food Engineering, 77(4), 926-935.
Dua, S., & Du, X. (2011). Data mining and machine learning in Cybersecurity. Taylor and Francis Group.
Ebrahimi, E., & Mollazade, K. (2010). Integrating fuzzy data mining and impulse acoustic techniques for almond nuts sorting. Australian Journal of Crop Science, 4(5), 353-358.
Felici, G., & Vercellis, C. (2008). Mathematical methods for knowledge discovery and data mining. Hershey, Pennsylvania, IGI Global.
Hall, M., Witten, I., & Frank, E. (2011). Data mining: Practical machine learning tools and techniques. Burlington: Kaufmann.
Huang, C. L., Liao, H. C., & Chen, M. C. (2008). Prediction model building and feature selection with support vector machines in breast cancer diagnosis. Expert Systems with Applications, 34(1), 578-587.
Huang, M. & Lu, R., (2010). Apple mealiness detection using hyperspectral scattering technique. Postharvest Biology and Technology, 58(3), 168-175.
 Huang, M., Zhu, Q., Wang, B., & Lu, R. (2012). Analysis of hyperspectral scattering images using locally linear embedding algorithm for apple mealiness classification. Computers and Electronics in Agriculture, 89, 175-181.
Mendoza, F., Lu, R., & Cen, H. (2014). Grading of apples based on firmness and soluble solids content using Vis/SWNIR spectroscopy and spectral scattering techniques. Journal of Food Engineering 125, 59–68.
Moshou, D., Wahlen, S., Strasser, R., Schenk, A., & Ramon, H. (2003). Apple mealiness detection using fluorescence
and self-organising maps. Computers and Electronics in Agriculture, 40(1), 103-114.
Omid, M. (2011). Design of an expert system for sorting pistachio nuts through decision tree and fuzzy logic classifier. Expert Systems with Applications, 38(4), 4339-4347.
Peneau, S., Brockhoff, P. B., Hoehn, E., Escher, F., & Nuessli, J. (2007). Relating consumer evaluation of apple freshness to sensory and physico-chemical measurements. Journal of Sensory Studies 22, 313–335.
Seppä, L., Peltoniemi, A., Tahvonen, R., & Tuorila, H. (2013). Flavour and texture changes in apple cultivars during storage. LWT-Food Science and Technology 54, 500–512.
Studman, C. J. (2001). Computers and electronics in postharvest technology—a review. Computers and Electronics in Agriculture, 30(1), 109-124.
Theodoridis, S., & Koutroumbas, K. (2009). Pattern Recognition (4th ed). Elsevier Inc.
Tiplica, T., Vandewalle, P., Verron, S., Grémy-Gros, C., & Mehinagic, E. (2010). Identification of apple varieties using acoustic measurements. In Conférence Internationale en Métrologie (CAFMET'10), p.103. Egypt: Cairo,
Unay, D., Gosselin, B., Kleynen, O., Leemans, V., Destain, M. F. & Debeir, O. (2011). Automatic grading of Bi-color,ed apples by multispectral machine vision. Computers and Electronics in Agriculture, 75(1), pp.204-212.
Valero, C., Barreiro, P., Ruiz-Altisent, M., Cubeddu, R., Pifferi, A., Taroni, P., Torricelli, A., Valentini, G., Johnson, D., & Dover, C. (2005). Mealiness detection in apples using time resolved reflectance spectroscopy. Journal of Texture Studies, 36(4), 439-458.
Zhang, W., Cui, D., & Ying, Y. (2014). Nondestructive measurement of pear texture by acoustic vibration method. Postharvest Biology and Technology, 96, 99-105.