Image Feature Extraction and Online Grading Method for Weight and Shape of Strawberry

doi:10.16182/j.issn1004731x.joss.17-0047

Abstract

Abstract: To deal with the classification problems of strawberry in production, a machine vision based strawberry weight and shape grading method was proposed. The strawberry image was segmented by thresholding to extract the fruit. The area and perimeter parameters of the fruit were then calculated and used to build the strawberry weight grading model through regression analysis. Elliptic Fourier descriptor was used to extract the shape features of the fruit, and these shape features were applied to train a support vector machine (SVM) which represented the strawberry shape grading model. 200 samples of strawberries were selected to test both models, and the results showed that the weight grading accuracy was 89.5%, the shape grading accuracy was 96.7%, and the average calculation time were 64 ms and 39 ms, respectively. Therefore, the approaches for grading strawberries were robust and effective.

Key words: machine vision, strawberry, grading, convex hull, elliptic Fourier descriptor, support vector machine

CLC Number:

TP391.41

Zhang Qing, Zou Xiangjun, Lin Guichao, Sun Yanhui. Image Feature Extraction and Online Grading Method for Weight and Shape of Strawberry[J]. Journal of System Simulation, 2019, 31(1): 7-9.

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