Visible Light Image-Based Method for Sugar Content Classification of Citrus

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

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.

Preliminary research on total nitrogen content prediction of sandalwood using the error-in-variable models based on digital image processing

This paper presents a method for predicting the total nitrogen content in sandalwood using digital image processing. The goal of this study is to provide a real-time, efficient, and highly automated nutritional diagnosis system for producers by analyzing images obtained in forests. Using images acquired from field servers, which were installed in six forest farms of different cities located in northern Hainan Province, we propose a new segmentation algorithm and define a new indicator named “growth status" (GS), which includes two varieties: GS_MER (the ratio of sandalwood pixels to the minimum enclosing rectangle pixels) and GS_MCC (the ratio of sandalwood pixels to minimum circumscribed circle pixels). We used the error-in-variable model by considering the errors that exist in independent variables. After comparison and analysis, the obtained results show that (1) The b and L channels in the Lab color system have complementary advantages. By combining this system with the Otsu method, median filtering and a morphological operation, sandalwood can be separated from the background. (2) The fitting degree of the models improves after adding the GS indicator and shows that GS_MCC performs better than GS_MER. (3) After using the error-in-variable model to estimate the parameters, the accuracy and precision of the model improved compared to the results obtained using the least squares method. The optimal model for predicting the total nitrogen content is y=237.374e−(4.471LL′+11.927aa′+2.782bb′)+26.248GSMCC−4.274. This study demonstrates the use of Internet of Things technology in forestry and provides guidance for the nutritional diagnosis of the important sandalwood tree species.