Optimization of raisin sorting machine using genetic algorithm

Sorting agricultural products refers to grading food and other crops based on size, color, appearance, and other factors such as separating impurities, fruits, and damaged and rotten products. Today, sorting technology and related equipment for grading agricultural crops are progressing in developed countries, which can be found in most large agricultural units. Therefore, initial packaging and transportation of the product are facilitated, and more added value can be provided for farmers. This study aimed to optimize the raisin sorting machine based on a genetic algorithm to increase the quality of raisin grading. Therefore, a seedless white variety of grape samples were randomly selected and prepared from an orchard in Makan, East Azerbaijan, Iran. Digital image processing techniques such as the image processing toolbox in MATLAB were used to extract features from an image for sorting. Other meta-heuristic algorithms such as PSO, differential evolution, and artificial bee colony algorithm were used to evaluate the accuracy of the results. According to the results, the artificial bee colony algorithm had better accuracy than other algorithms, but the convergence speed was lower, and the computational volume was higher. However, the genetic and PSO algorithms had an accuracy almost equal to the artificial bee colony algorithm despite having a higher speed of convergence and lower computational operations, which can be used as the best algorithm in this application. Differential evolutionary algorithms and harmony search require processing in many iterations, and the computation time is not economical. Therefore, the clustering of raisins in industrial units requires high clustering speed and minimum error to avoid discarding or outliers, and genetics and PSO algorithms were acceptable.