A Computer Vision System For Defect Discrimination And Grading In Tomatoes Using Machine Learning And Image Processing

With large-scale production,and the need for high-quality tomatoes to meet consumer,market standards and expectations,has led to the need for an inline,accurate,reliable grading system during post-harvest.Development and use of machine vision technology in tomato sorting has the potential to increase tomato commercial value while reducing labor costs.A image recognition system that relies around the correct optics and camera resolution can easily examine specifics of an object that are too small for the human eye to see.The general results of the study are summarized as follows:?.Based on a low-cost monochromatic camera and Arduino microcontroller,an automated tomato sorting system was set up to grade on two features:size and color.The monochromatic image was first processed into a binary image followed by size and color discrimination.?.Through ellipse fitting and setting thresholds for large,medium and small tomatoes,discrimination in size was achieved.Dominant color method and setting a threshold for deep and light red color intensities obtained color discrimination.?.Another study introduced a tomato grading machine vision system based on RGB images.The proposed system performed calyx and stalk scar detection at an average accuracy of 0.9515 for both defected and healthy tomatoes by histogram thresholding based on the mean g-r value of these regions of interest.Defected regions were detected by an RBF-SVM classifier using the LAB color-space pixel values,the model achieved an overall accuracy of 0.989 upon validation.?.Four grading categories recognition models were developed based on color and texture features.the RBF-SVM outperformed all the explored models with the highest accuracy of 0.9709 for healthy and defected category.However,the grading accuracy decreased as the number of grading categories increased.A combination of color and texture features achieved the highest accuracy in all the grading categories in image features evaluation.This proposed system can be used as an inline tomato sorting tool to ensure quality standards are adhered to and maintained.The results shows that the proposed system is accurate,fast and feasible...