4.7 Article

Detecting Maturity in Fresh Lycium barbarum L. Fruit Using Color Information

期刊

HORTICULTURAE
卷 7, 期 5, 页码 -

出版社

MDPI
DOI: 10.3390/horticulturae7050108

关键词

L; barbarum; maturity detection; color information; Mahalanobis distance; image processing; support vector machine; picking force

资金

  1. National Key Research and Development Program of China [2018YFD0701102]
  2. China Scholarship Council

向作者/读者索取更多资源

A quantitative maturity detection model for fresh Lycium barbarum fruit was successfully established using image processing methods and support vector machine model. Field experiments verified the accuracy of the model's predictions in relation to the picking forces of the fruit.
The accurate quantitative maturity detection of fresh Lycium barbarum L. (L. barbarum) fruit is the key to determine whether fruit are suitable for harvesting or not and can also be helpful to improve the quality of post-harvest processing. To achieve this goal, abnormal samples were eliminated by the Mahalanobis Distance (MD), and nine components (i.e., R, G, B, H, S, V, L, a, and b) of the ripe fruit, half-ripe fruit, and unripe fruit were extracted, firstly. Then, significant component combinations of the three fruits beneficial to the extraction of their areas were determined. Through binary processing, morphology processing, and other image processing methods, a quantitative maturity detection model of fruit was established based on the support vector machine (SVM) model. On this basis, field experiments were conducted to verify and compare the relationship between the prediction results of the model and the picking forces of fruit. Field experiments showed that the accuracies of both the training set and prediction set were 100% and the prediction results of the model were consistent with the picking forces of fruit. Findings provided a theoretical basis for the accurate quantitative maturity detection of fresh L. barbarum fruit.

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