Non-destructive monitoring of netted muskmelon quality based on its external phenotype using Random Forest.

Abstract

The internal phenotypes of netted muskmelon (Cucumis melo L. var. eticulates Naud.) are always associated with its external phenotypes. In this study, the parameters of external phenotypic traits were extracted from muskmelon images captured by machine vision, and the internal phenotypes of interest to us were measured. Pearson analysis showed that most external phenotypic traits were highly correlated with these internal phenotypes in muskmelon fruit. In this study, we used the random forest algorithm to predict muskmelon fruit internal phenotypes based on the significantly associated external parameters. Carotenoids, sucrose, and total soluble solid (TSS) were the three most accurately monitored internal phenotypes with prediction R-squared (R2) values of 0.947 (root-mean-square error (RMSE) = 0.019 mg/100 g), 0.918 (RMSE = 3.233 mg/g), and 0.916 (RMSE = 1.089%), respectively. Further, a simplified model was constructed and validated based on the top 10 external phenotypic parameters associated with each internal phenotype, and these parameters were filtered with the varImp function from the random forest package. The top 10 external phenotypic parameters correlated with each internal phenotype used in the simplified model were not identical. The results showed that the simplified models also accurately monitored the melon internal phenotypes, despite that the predicted R2 values decreased 0.3% to 7.9% compared with the original models. This study improved the efficiency and accuracy of real-time fruit quality monitoring for greenhouse muskmelon.