A machine vision-based system for measuring the chromatic parameters of bell pepper using artificial neural networks

The appearance color of bell pepper is significantly related to its quality which affects consumer’s acceptance to buy.Also, homogeneity of color is among the most essential export standards of bell peppers. Commercially standard colorimetric devices are very expensive and are often available in scientific research centers. The aim of the present study was to develop and calibrate a simple, cheap, and portable machine vision (MV)-based system to accurately measure the chromatic parameters of bell peppers. For this purpose, a MV system possessing with a digital CCD camera, and an artificial lighting system was developed. To calibrate the color of the utilized camera, the standard color cards were used. An appropriate algorithm based on image processing techniques was developed to compute the chromatic parameters of the crop in the CIELAB color space. The development system was calibrated and compared with a standard colorimetric device using multi-layer perceptron (MLP) artificial neural networks (ANNs) models. The optimum ANN model was employed in diagnose of the chromatic properties of bell peppers using the developed MV system. The overall accuracy of the proposed MV system was ۸۲.۹% in comparison with the standard device. The results showed that the proposed system can be considered as a more reliable device compared to traditional commercial devices and could be a suitable alternative in the absence of a specialized color measurement device

Keywords:

Bell peppers , Colorimetry , CIELAB color space , Image processing , Machine vision , Artificial neural networks , Calibration.