Application of Artificial Neural Network and Non-Destructive CT scan Test in Estimating the Amount of Pear Bruise Due To External Loads

Azadbakht, Mohsen; Vahedi Torshizi, Mohammad

doi:10.22101/JRIFST.2019.07.22.826

Application of Artificial Neural Network and Non-Destructive CT scan Test in Estimating the Amount of Pear Bruise Due To External Loads

Document Type : Original Paper

Authors

¹ Associate Professor, Department of Bio-System Mechanics, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

² Master Student, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

10.22101/JRIFST.2019.07.22.826

Abstract

Pear damage is one of the main causes of the loss of fruit quality. Bruises occur during dynamic and quasi-static loading, which causes damage to the healthy tissue of the fruit. In this research, pears were placed under quasi-static loading (thin edge and wide edge) and dynamic loading. Then they were stored in 5, 10 and 15 days and after each storage period, using the CT-Scan non-destructive technique the bruise percentage was estimated. In this study, multi-layer perceptron artificial neural network (MLP) by 2 hidden layers and 3, 5, 7 and 9 neurons hidden layers was selected for modeling of loading force and storage period to predict bruise rate. The highest R2 values for training and testing for quasi-static loading of thin edge and wide edge in a 9-neural network were training Thin-edge=0.91, test Thin-edge =0.99 and training Wide-edge=0.95, test Wide-edge =0.99. For the dynamic loading of a network with 3 neurons in the hidden layer has the highest value (training Wide-edge=0.98, test Wide-edge =0.99). For learning (9 neurons) quasi-static loading thin edge (7 neurons) quasi-static loading wide edge and dynamic loading (7 neurons) have been the best network. According to the results obtained for R², RMSE and learning cycle, it can be said that the neural network has the ability to predict the bruise percentage to an acceptable level for pears.

Keywords

References

Azadbakht, M., Aghili, H., Ziaratban, A., & Torshizi, M. V. (2017). Application of artificial neural network method to exergy and energy analyses of fluidized bed dryer for potato cubes. Energy, 120, 947-958. doi:https://doi.org/10.1016/j.energy.2016.12.006

Azadbakht, M., Torshizi, M. V., Ziaratban, A., & Ghajarjazi, E. (2016). Application of Artificial Neural Network (ANN) in predicting mechanical properties of canola stem under shear loading. Agricultural Engineering International: CIGR Journal, 18(2), 413-425.

Balogun, W. A., Salami, M.-J. E., Aibinu, A. M., Mustafah, Y. M., & Isiaka.B.S, S. (2014). Mini Review: Artificial Neural Network Application on Fruit and Vegetables Quality Assessment. International Journal of Scientific & Engineering Research, 5(6), 702-708.

Chakespari, A., Rajabipour, A., & Mobli, H. (2010). Mass modeling of two apple varieties by geometrical attributes. Australian Journal of Agricultural Engineering, 1(3), 112.

Diels, E., van Dael, M., Keresztes, J., Vanmaercke, S., Verboven, P., Nicolai, B., . . . Smeets, B. (2017). Assessment of bruise volumes in apples using X-ray computed tomography. Postharvest Biology and Technology, 128, 24-32. doi:https://doi.org/10.1016/j.postharvbio.2017.01.013

Fathi, M., Mohebbi, M., & Razavi, S. M. A. (2011). Application of image analysis and artificial neural network to predict mass transfer kinetics and color changes of osmotically dehydrated kiwifruit. Food and Bioprocess Technology, 4(8), 1357-1366. doi:https://doi.org/10.1007/s11947-009-0222-y

Ganiron, T. U. (2014). Size properties of mangoes using image analysis. International Journal of Bio-Science and Bio-Technology, 6(2), 31-42.

Hernández-Sánchez, N., Moreda, G. P., Herre-ro-Langreo, A., & Melado-Herreros, Á. (2016). Assessment of internal and external quality of fruits and vegetables. In N. Sozer (Ed.), Imaging Technologies and Data Processing for Food Engineers. Food Engineering Series (pp. 269-309): Springer, Cham.

Khoshnevisan, B., Rafiee, S., Omid, M., & Yousefi, M. (2013). Prediction of environmental indices of Iran wheat production using artificial neural networks. International Journal of Energy & Environment, 4(2).

Kolniak-Ostek, J. (2016). Identification and quantification of polyphenolic compounds in ten pear cultivars by UPLC-PDA-Q/TOF-MS. Journal of Food Composition and Analysis, 49, 65-77. doi:https://doi.org/10.1016/j.jfca.2016.04.004

Kotwaliwale, N., Singh, K., Kalne, A., Jha, S. N., Seth, N., & Kar, A. (2014). X-ray imaging methods for internal quality evaluation of agricultural produce. Journal of Food Science and Technology, 51(1), 1-15. doi:https://doi.org/10.1007/s13197-011-0485-y

Liu, Y., & Ying, Y. (2007). Noninvasive method for internal quality evaluation of pear fruit using fiber-optic FT-NIR spectrometry. International Journal of Food Properties, 10(4), 877-886. doi:https://doi.org/10.1080/10942910601172042

Massah, J., Hajiheydari, F., & Haddad, D. (2017). Application of Electrical Resistance in Nondestructive Postharvest Quality Evaluation of Apple Fruit. Journal of Agricultural Science and Technology, 19, 1031-1039.

Pan, L., Zhang, Q., Zhang, W., Sun, Y., Hu, P., & Tu, K. (2016). Detection of cold injury in peaches by hyperspectral reflectance imaging and artificial neural network. Food Chemistry, 192, 134-141. doi:https://doi.org/10.1016/j.foodchem.2015.06.106

Pérez-Jiménez, J., & Saura-Calixto, F. (2015). Macromolecular antioxidants or non-extractable polyphenols in fruit and vegetables: Intake in four European countries. Food Research International, 74, 315-323. doi:https://doi.org/10.1016/j.foodres.2015.05.007

Rostampour, V., Motlagh, A. M., Komarizadeh, M. H., Sadeghi, M., Bernousi, I., & Ghanbari, T. (2013). Using Artificial Neural Network (ANN) technique for prediction of apple bruise damage. Australian Journal of Crop Science, 7(10), 1442.

Wang, Z., Hu, M., & Zhai, G. (2018). Application of deep learning architectures for accurate and rapid detection of internal mechanical damage of blueberry using hyperspectral transmittance data. Sensors, 18(4), 1126. doi:https://doi.org/10.3390/s18041126

Zarifneshat, S., Rohani, A., Ghassemzadeh, H. R., Sadeghi, M., Ahmadi, E., & Zarifneshat, M. (2012). Predictions of apple bruise volume using artificial neural network. Computers and Electronics in Agriculture, 82, 75-86. doi:https://doi.org/10.1016/j.compag.2011.12.015