Automatic Grading of Emperor Apples Based on Image Processing andANFIS

Mass-based fruit classification is important in terms of improving packaging and marketing. Mass sizing can beaccomplished by direct or indirect methods. In this study, 100 samples of Emperor Apples were randomly selected from an orchard in Kermanshah, Iran (longitude: 7.03 °E; latitude: 4.22 °N). All tests were carried out in Physical Laboratory, Faculty of Agriculture Engineering, Razi University, and Kermanshah, Iran. Fourteen parameters were obtained by image processing for each apple. Several mass modeling were made using ANFIS and linear regression methods. In the best model for ANFIS, linear and nonlinear regression, R2, SSE, and MSE were 0.990, 276.58, 13.17, 0.856, 15980.96,166.47 and 0.791, 24512.16, 255.35, respectively. So, a mass-based sorting system was proposed with machine vision system and using ANFIS method that could obtain apple mass without contact with the fruit. Benefits of this system over mechanical and electrical systems were: 1- Easier recalibration of the machine to the groups with different sizes, and2- Reaching more accurate mass measurement and higher operating speed using indirect grading.