A machine vision-intelligent modelling based technique for in-line bell pepper sorting

Abstract

The uniformity of appearance attributes of bell peppers is significant for consumers and food industries. To automate the sorting process of bell peppers and improve the packaging quality of this crop by detecting and separating the not likable low-color bell peppers, developing an appropriate sorting system would be of high importance and influence. According to standards and export needs, the bell pepper should be graded based on maturity levels and size to five classes. This research has been aimed to develop a machine vision-based system equipped with an intelligent modelling approach for in-line sorting bell peppers into desirable and undesirable samples, with the ability to predict the maturity level and the size of the desirable bell peppers. Multilayer perceptron (MLP) artificial neural networks (ANNs) as the nonlinear models were designed for that purpose. The MLP models were trained and evaluated through five-fold cross-validation method. The optimum MLP classifier was compared with a linear discriminant analysis (LDA) model. The results showed that the MLP outperforms the LDA model. The processing time to classify each captured image was estimated as 0.2 s/sample, which is fast enough for in-line application. Accordingly, the optimum MLP model was integrated with a machine vision-based sorting machine, and the developed system was evaluated in the in-line phase. The performance parameters, including accuracy, precision, sensitivity, and specificity, were 93.2%, 86.4%, 84%, and 95.7%, respectively. The total sorting rate of the bell pepper was also measured as approximately 3 000 samples/h.