Detection and Classification of Bruises on ‘Red Delicious’ Apples Using Active Thermography

Document Type : Research Paper


1 Departement of Biosystem Engineering, faculty of Agriculture, urmia university, urmia, iran

2 Departement of Biosystem Engineering, Faculty of Agriculture, urmia univercity, urmia, iran


Identifying and sorting healthy agricultural products from damaged products reduces casualties and losses caused by the spread of disease to unhealthy samples. Due to Iran's insignificant contribution to agricultural exports, the use of non-destructive methods, such as thermography in the sorting and grading of the fruit, is necessary. In this research, active thermography method was used to identify the Red delicious cultivars of apple. In order to study the temperature variations of apples, used a factorial experiment was conducted in a randomized complete design with three independent variables including bruises, heating temperature and cooling time. The results of variance analysis of surface temperature of healthy and bruise apples showed that the levels of bruises, heating temperature and cooling time on the surface temperature of bruises apples had a significant effect at 1% probability. Statistical and texture features were extracted from thermal images and the artificial neural network method was used to classify two healthy classes and bruised. Neural network accuracy with a hidden layer and 15 neurons was obtained 100%. The results indicate that the difference in temperature of the bruised and sound tissue can be considered as a criterion for the classification of apples. Also, Active thermography is an efficient and high-tech method for detecting apple bruises.


Main Subjects

Baranowski, P., Lipecki, J., Mazurek, W., Walczak, R.T., 2008. Detection of watercore in ‘Gloster’apples using thermography. Postharvest biology and technology 47, 358-366.
Barreiro, P., Ortiz, C., Ruiz‐Altisent, M., Schotte, S., Andani, Z., Wakeling, I., Beyt, P. (1998). Comparison between sensory and instrumental measurements for mealiness assessment in apples. A collaborative test. Journal of Texture Studies 29, 509-525.
Chelladurai, V., Jayas, D., White, N., 2010. Thermal imaging for detecting fungal infection in stored wheat. Journal of stored products research 46, 174-179.
Dosti, I., Golzarian, A., Aghkhani, M.R., & Sadrnia, M. (2015). Study the changes in the color and temperature of the apple texture during time using the visual and thermal images processing. Iranian Journal of Food Science and Technology.11 (5), 677-693. In Farsi
Dosti, I., Golzarian, A., Aghkhani, M.R., & Sadrnia, M. (2013).  A review of thermal imaging applications in the analysis of agricultural products quality. 8th National Congress on Agricultural Machinery Engineering,Mashhad University, Mashhad, Iran, pp. 534-542.
Du, C.-J., & Sun, D.-W. 2004. Recent developments in the applications of image processing techniques for food quality evaluation. Trends in Food Science & Technology, 15, 230-249.
FAO, 2011. FAOSTAT database.
Hellebrand, H.J., Beuche, H., Linke, M. (2002). Thermal Imaging. Physical Methods in Agriculture. Springer, pp. 411-427.
Hellebrand, H., Herppich, W., Beuche, H., Dammer, K., Linke, M., Flath, K. (2006). Investigations of plant infections by thermal vision and NIR imaging. International agrophysics 20, 1.
Jamil, N., Bejo, S.K. (2014). Husk Detection Using Thermal Imaging Technology, Agriculture and Agricultural Science Procedia,128 – 135.
Leemans, V. & Destain, M. F. 2004. A real-time grading method of apples based on features extracted from defects. Journal of Food Engineering, 61, 83-89.
Lima, R.S.N., Garcia-Tejero, I., Lopes,T.S., Costa, J.M., Vaz, m.,and Duran-Zuazo, V.H. (2015). Linking thermal imaging to physiological indicators in Carica papaya, under different watering regimes. Agricultural Water Management 164,148–157.
Manickavasagan, A., Jayas, D. S., White, N. D. G., & Paliwal, J. 2010a. Wheat Class Identification Using Thermal Imaging. Food Bioprocess Technol, 3, 450–460
Qing, Z., Ji, B., Zude, M. (2007). Wavelength selection for predicting physicochemical properties of apple fruit based on near‐infrared spectroscopy. Journal of food quality 30, 511-526.
Stockton, G.R. and Lucas, R.G. (2012), Using aerial infrared thermography to detect utility theft of service, SPIE Defense, Security, and Sensing, International Society for Optics and Photonics.
Teimouri, N., Omid, M., Mollazade, K., Rajabipour, A., 2015. Separation of clinging almonds and their qualitative classification by combining image processing techniques and artificial neural networks. Biosystem engineering of Iran 46, 355-362. (In Farsi)
Varith, J., Hyde, G., Baritelle, A., Fellman, J., Sattabongkot, T. (2003). Non-contact bruise detection in apples by thermal imaging. Innovative Food Science & Emerging Technologies 4, 211-218.
Van Zeebroeck, M., Tijskens, E., Dintwa, E., Kafashan, J., Loodts, J., De Baerdemaeker, J., Ramon, H. (2006). The discrete element method (DEM) to simulate fruit impact damage during transport and handling: model building and validation of DEM to predict bruise damage of apples. Postharvest Biology and Technology 41, 85-91.
Veraverbeke, E. A., Verboven, P., Lammertyn, J., Cronje, P., Baerdemaeker, J. D., & Nicolaı, B. M. 2006. Thermographic surface quality evaluation of apple. Journal of Food Engineering, 77, 162-168.
Xing, J., Baerdamaker, J. (2005). Fresh Bruise detection on selected cultivars apples using visible and NIR spectroscopy. Information and Technology for Sustainable Fruit and Vegetable Production. Frutic 5, 503-507.
Zhang, B., Huang, W., JiangboLi, Zhao, C., Fan, S., Wu, J., & Liu, C. 2014. Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Research International, 62, 326-343.