Potential use of machine vision technique for qualitative separation of walnut kernel (Kaghazi Walnut)

Nowadays, application of machine vision techniques had been extensively used in agriculture and particularly in food industries. This system can be used in quality separation, especially for valuable products such as walnut. The high nutritional value of walnuts has caused the crop to be widely processed in many processed foods. The texture is one of the most important features of agricultural crops which has been widely applied in food industry for quality evaluation. The texture of images reflects changes in pixel intensity values, which may include information from the geometric structure of objects. In this study, the possibility of walnut separating in three categories, based on quality, including: light-intact , dark-intact and damaged kernel using image processing and color systems such as RGB, HSV and L*a*b* on Kaghazi varieties was investigated. The machine vision system includes a lighting box, a camera (model SC-W12 SONY with resolution of 4 mega pixels), a computer and MATLAB software. So that, all samples are captured in RGB color system, then using transfer functions the other color systems components were calculated. Also, separation of intact samples from non-intact was evaluated using statistical analyzing on color space of RGB, L*a*b* and HSV. The results showed that in the RGB color space using of components of R (redness intensity) and G (green color intensity), and in the HSV color space based on component H and V, separation of healthy samples was possible. The success of this method was 92%. Also, in the L*a*b* color space, components of L* and b* be able to clear healthy samples from the other two categories. The success of this method was 90%. However, separating dark-intact samples from damaged samples were not possible because of overlapping of colors area. In case of surface tissue indices, contrast and energy were able to separate intact samples from non-intact samples.