A visible-range computer-vision system for automated, non-intrusive assessment of the pH value in Thomson oranges

作者：Sabzi Sajad; Ignacio Arribas Juan

来源：Computers in Industry, 2018, 99: 69-82.

DOI：10.1016/j.compind.2018.03.016

• Computer vision
• Estimation
• Fruit grading
• Imperialist competitive algorithm
• Neural network
• Optimization
• pH value
• Regression
• Response surface methodology
• Thomson orange

Fruit may be classified for the purposes of usage, packaging and marketing based on the pH (potential of hydrogen) value-a numeric scale used to specify the acidity or basicity of an aqueous solution measured in units of moles per liter of hydrogen ions. In this study, a new approach for the automated and non-intrusive estimation of the pH value of the Thomson navel orange (CRC 969, Citrus sinensis) fruit is presented based on visible-range image processing, image feature extraction and with the use of hybrid imperialist competitive algorithm (ICA)-artificial neural network (ANN) regression. Image features studied include length, width, area, eccentricity, perimeter, blue-value, green-value, red-value, width, contrast, texture, roughness and several ratios thereof. Principal component analysis (PCA) is applied to reduce the number of dimensions without loss of important information and a cubic polynomial function of the mean square error (MSE) versus several factors is computed using the response surface methodology (RSM) approach. Results for pH prediction are given and compared with true measured pH values over the entire 100 Thomson orange dataset, including estimated pH scatter regression plots and estimated pH boxplots. Cross validation is performed over 1000 repeated random trial experiments with uniform random train-and test-sample sets (80% training and 20% disjoint test samples). In addition, we provide numerical results based on the levels achieved by response surface methodology (RSM) evaluated over various error coefficients: the sum square error (SSE), the mean absolute error (MAE), the coefficient of determination (R-2), the root mean square error (RMSE), and MSE, resulting in R-2 = 0.843 +/- 0.043, MSE = 0.046 +/- 0.022, MAE = 0.166 +/- 0.039, SSE = 0.915 +/- 0.425, and RMSE = 0.214 +/- 0.146, over the test set. The results demonstrate that such an automated pH-based sorting system with machine vision using the hybrid ICA-ANN algorithm can accurately compute the pH value of Thomson oranges without any contact with the fruit, and which has clear potential applications in the food industry.

• 出版日期2018-8