An automatic and non-intrusive hybrid computer vision system for the estimation of peel thickness in Thomson orange

Hossein Javadikia, Sajad Sabzi, Juan I. Arribas


Orange peel has important flavor and nutrition properties and is often used for making jam and oil in the food industry. For previous reasons, oranges with high peel thickness are valuable. In order to properly estimate peel thickness in Thomson orange fruit, based on a number of relevant image features (area, eccentricity, perimeter, length/area, blue component, green component, red component, width, contrast, texture, width/area, width/length, roughness, and length) a novel automatic and non-intrusive approach based on computer vision with a hybrid particle swarm optimization (PSO), genetic algorithm (GA) and artificial neural network (ANN) system is proposed. Three features (width/area, width/length and length/area ratios) were selected as inputs to the system. A total of 100 oranges were used, performing cross validation with 100 repeated experiments with uniform random samples test sets. Taguchi’s robust optimization technique was applied to determine the optimal set of parameters. Prediction results for orange peel thickness (mm) based on the levels that were achieved by Taguchi’s method were evaluated in several ways, including orange peel thickness true-estimated boxplots for the 100 orange database and various error parameters: the sum square error (SSE), the mean absolute error (MAE), the coefficient of determination (R2), the root mean square error (RMSE), and the mean square error (MSE), resulting in mean error parameter values of R2=0.854±0.052, MSE=0.038±0.010, and MAE=0.159±0.023, over the test set, which to our best knowledge are remarkable numbers for an automatic and non-intrusive approach with potential application to real-time orange peel thickness estimation in the food industry.


machine learning; neural network; particle swarm optimization; stochastic analysis; peel thickness, skin

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Adelkhani A, Beheshti B, Minaei S, Javadikia P, Ghasemi-Varnamkhasti M, 2013. Taste characterization of orange using image processing combined with ANFIS. Measurement 46: 3573-3580.

Blasco J, Aleixos N, Molto E, 2003. Machine vision system for automatic quality grading of fruit. Biosyst Eng 85 (4): 415.

Fan FH, Ma Q, Ge J, Peng QY, Riley WW, Tang SZ, 2013. Prediction of texture characteristics from extrusion food surface images using a computer vision system and artificial neural networks. J Food Eng 118: 426-433.

Fu L, Sun S, Li R, Wang S, 2016. Classification of kiwifruit grades based on fruit shape using a single camera. Sensors 16: 1-14.

Gonzalez RC, Woods RE, Eddins SL, 2004. Digital image processing using MATLAB. Prentice Hall.

Homutova I, Blazek J, 2006. Differences in fruit skin thickness between selected apple (Malus domestica Borkh.) cultivars assessed by histological and sensory methods. Hort Sci (Prague) 33 (3): 108-113.

Jafari A. Fazayeli A, Zarezadeh MR, 2014. Estimation of orange skin thickness based on visual texture coarseness. Biosys Eng 117: 73-82.

Karimi N, Zandieh M, Najafi AA, 2011. Group scheduling in flexible flow shops: a hybridised approach of imperialist competitive algorithm and electromagnetic-like mechanism. Int J Prod Res 49 (16): 4965-4977.

Karimi H, Navid H, Mahmoudi A, 2015. Online laboratory evaluation of seeding-machine application by an acoustic technique. Span J Agric Res 13 (1): e0202.

Leiva-Valenzuela GA, Lu R, Aguilera JM, 2013. Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging. J Food Eng 115: 91-98.

Liu S, Xu L, Li D, Li Q, Jiang Y, Tai H, Zeng L, 2013. Prediction of dissolved oxygen content in river crab culture based on least squares support vector regression optimized by improved particle swarm optimization. Comput Electron Agr 95: 82-91.

Marchal PC, Gila DM, García JG, Ortega JG, 2013. Expert system based on computer vision to estimate the content of impurities in olive oil samples. J Food Eng 119: 220-228.

Mellit A, Kalogirou SA, 2014. MPPT-based artificial intelligence techniques for photovoltaic systems and its implementation into field programmable gate array chips: Review of current status and future perspectives. Energy 70: 1-21.

Mitchell M, 1999. An introduction to genetic algorithms. The MIT Press, Cambridge, MA, USA.

Modenes AN, Espinoza-Quiñones FR, Trigueros DEG, Pietrobelli JM, Lavarda FL, Ravagnani MA, Bergamasco R, 2012. Binary adsorption of a Zn(II)-Cu(II) mixture onto Egeria densa and Eichhornia crassipes: Kinetic and equilibrium data modeling by PSO. Sep Sci Technol 47: 875-885.

Mohapatra A, Shanmugasundaram S, Malmathanraj R, 2017. Grading of ripening stages of red banana using dielectric properties changes and image processing approach. Comput Electron Agr 143: 100-110.

Morgan KT, Rouse RE, Roka FM, Futch SH, Zerri M, 2005. Leaf and fruit mineral content and peel thickness of 'Hamlin' orange. Proc Fla State Hort Soc 118: 19-21.

Mottaghitalab M, Nikkhah M, Darmani-Kuhi H, López S, France J, 2015. Predicting methionine and lysine contents in soybean meal and fish meal using a group method of data handling-type neural network. Span J Agric Res 13 (1): e0601.

Naganur HG, Sannakki SS, Rajpurohit VS, Arunkumar R, 2012. Fruits sorting and grading using fuzzy logic. Int J Adv Res Comput Eng Technol 1 (6): 117-122.

Phadke MS, 1989. Quality engineering using robust design. Prentice-Hall, Englewood Cliffs, NJ, USA.

Rio-Segade S, Giacosa S, Gerbi V, Rolle L, 2011. Berry skin thickness as main texture parameter to predict anthcyanin extractability in winegrapes. Food Sci Technol 44: 192-398.

Rungpichayapichet P, Nagle M, Yuwanbun P, Khuwijitjaru P, Mahayothee B, Muller J, 2017. Prediction mapping of physicochemical properties in mango by hyperspectral imaging. Biosyst Eng 159: 109-120.

Sabzi S, Javadikia P, Rabani H, Adelkhani A, 2013. Mass modeling of Bam orange with ANFIS and SPSS methods for using in machine vision. Measurement 46: 3333-3341.

Shin JS, Lee WS, Ehsani R, 2012. Postharvest citrus mass and size estimation using a logistic classification model and a watershed algorithm. Biosyst Eng 113: 42-53.

Shokrollahpour E, Zandieh M, Dorri B, 2011. A novel imperialist competitive algorithm for bi-criteria scheduling of the assembly flowshop problem. Int J Prod Res 49: 3087-3103.

Thendral R, Suhasini A, 2017. Automated skin defect identification system for orange fruit grading based on genetic algorithm. Current Sci 112 (8): 1704-1711.

Tong JH, Li JB, Jiang HY, 2013. Machine vision techniques for the evaluation of seedling quality based on leaf area. Biosyst Eng 115: 369-379.

Wen Sun D, 2006. Thermal food processing. Taylor & Francis, NY.

Williams VL, Witkowski ETF, Balkwill K, 2007. Relationhip between bark thickness and diameter at breast height of six tree species used medicinally in South Africa. S Afr J Bot 73: 449-465.

Yuan J, He C, Gao W, Lin J, Pang Y, 2014. A novel hard decision decoding scheme based on genetic algorithm and neural network. Optik 125 (14): 3457-3461.

DOI: 10.5424/sjar/2018164-11185