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

  • Hossein Javadikia Razi University, College of Agriculture and Natural Resources, Dept. Mechanical Engineering of Biosystems, Kermanshah
  • Sajad Sabzi University of Mohaghegh Ardabili, College of Agriculture, Dept. Biosystems Engineering, Ardabil
  • Juan I. Arribas University of Valladolid, Dept. Teoría de la Señal y Comunicaciones, 47011 Valladolid University of Salamanca, Instituto de Neurociencias de Castilla-León, 37007 Salamanca
Keywords: machine learning, neural network, particle swarm optimization, stochastic analysis, peel thickness, skin


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.


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Author Biography

Juan I. Arribas, University of Valladolid, Dept. Teoría de la Señal y Comunicaciones, 47011 Valladolid University of Salamanca, Instituto de Neurociencias de Castilla-León, 37007 Salamanca


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How to Cite
Javadikia, H., Sabzi, S., & Arribas, J. I. (2019). An automatic and non-intrusive hybrid computer vision system for the estimation of peel thickness in Thomson orange. Spanish Journal of Agricultural Research, 16(4), e0204.
Agricultural engineering