Carrot grading system using computer vision feature parameters and a cascaded graph convolutional neural network

Recent technological development in the computer vision field has provided opportunities for agriculture applications. The field of computer vision allows a machine to "see," providing an alternative to using the human eye to identify, measure, and track a target in image processing. Computer vision technology uses a sensor, camera, and computer. The carrot is one of the most important vegetables in the world, and a grading scale for carrots will improve market competitiveness. In marketing and carrot processing, carrot grading plays a major part. Existing approaches using the traditional carrot grading system, which requires manual involvement, are inefficient and labor-intensive. The computer vision feature parameters of the carrot, such as its length, average diameter, maximum diameter, aspect ratio, perimeter, and area, are extracted, along with RGB (Red, Green, Blue) and HSV (Hue, Saturation, Value) color parameters. These 12 computer vision parameters are given as input to a recognition model using the proposed optimized cascaded graph convolutional neural network with Bayesian optimization. The statistical results of the experiments performed with this model show that the proposed recognition system with computer vision feature parameters can grade carrots accurately. (c) 2022 SPIE and IS&T