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Determination of shape features for apricot based on machine vision
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: 2018 ASABE Annual International Meeting 1800739.(doi:10.13031/aim.201800739)
Authors: Xi Yang, Ruoyu Zhang, Kunlin Zou
Keywords: apricot; fruit shape; machine vision; machine learning
Abstract. Apricot is the third most important stone fruit in the world. Its shape is one significant element to describe characteristics. However, there are no common methods or standards to describe fruit shape in the world. According to the rules of species and the standards of market detection, there is still need a more precise uniform standard to describe the fruit shape qualitatively and quantitatively. In this study, the shape features of four apricot species were investigated. To simplify geometric attributes, line items were used to describe the fruit shape. Based on the properties of shape, apricot species relationship was studied by cluster analysis. The identification of apricot species was tried by three machine learning methods consisting of partial least squares discriminant analysis (PLS_DA) , support vector machine (SVM), and back-propagation neural network (BP_NN). Accuracy of PLS_DA, SVM and BP_NN were 95.5, 99 and 96.5 %. Thus, quantitative research could be done in this way.
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