Machine vision based expert system to estimate orange mass of three varieties

Hossein Javadikia, Sajad Sabzi, Hekmat Rabbani


A key issue in fruit export is classification and sorting for acceptable marketing. In the present work, the image processing technique was employed to grade three varieties of oranges (Bam, Khooni and Thompson) separately. The reason for choosing this fruit as the object of the study was its abundant consumption worldwide. In this study, 14 parameters were extracted: area, eccentricity, perimeter, length/area, blue value, green value, red value, width, contrast, texture, width/area, width/length, roughness, and length. Further, the ANFIS (Adaptive Network-based Fuzzy Inference System) method was utilized to estimate the orange mass from the data obtained using the image processing in three varieties. In ANFIS model, samples were divided into two sets, one with 70% for training set and the other one with 30% for testing set. The results of the present study demonstrated that the coefficient of determination (R2) of the best model for Bam, Khooni and Thompson measured 0.948, 0.99, and 0.98, respectively. In addition, the results indicated that the estimation accuracy of the best model for Bam, Khooni and Thompson was measured as ±3.7 g, ±1.28 g, ±3.2 g, respectively. This result was very satisfactory for the application of ANFIS to estimate the orange mass.
Keywords: ANFIS, orange, machine vision, mass, sorting
DOI: 10.3965/j.ijabe.20171002.1737

Citation: Javadikia H, Sabzi S, Rabbani H. Machine vision based expert system to estimate orange mass of three varieties. Int J Agric & Biol Eng, 2017; 10(2): 132–139.


ANFIS, orange, machine vision, mass, sorting


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