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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.

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