Automated Quality Control Framework for Solanum lycopersicum using Image Processing

There is a greater interest from vegetable/ organic product enterprises to keep up the quality of their product to a satisfactory level. These ventures are inconsistent research for a further advanced approach to maintain the quality of their item. The use of image processing is one such way. Though the food processing mechanism has evolved, the issue with such a framework is that of quality control. The use of image processing in the area of quality control could help in boosting the quality standards of the enterprise. This paper goal at applying image processing techniques in achieving effective quality maintenance in Solanum Lycopersicum commonly termed as tomato processing industries. The framework assesses the maturity of Solanum Lycopersicum based on color. The proposed architecture includes video/image acquisition, enhancement, and feature extraction, training, and classification techniques in the proposed system design. The acquired images/video frames have been converted to Hue Saturation Value (HSV) color space to improve the quality of the image.