Abstract
New research on Machine Learning is th e subject of a report. According to news reporting from Beijing, People's Republ ic of China, by NewsRx journalists, research stated, "Jujube is susceptible to b iotic and abiotic adversity stresses resulting in abnormal phenotypic defects. T herefore, abnormal phenotype fruits should be removed during postharvest sorting to increase added value." The news correspondents obtained a quote from the research from China Agricultur al University, "An improved maximum horizontal diameter linear regression (MHD-L R) method for size grading of jujube prior to detection of abnormal phenotypic d efects was developed. The accuracy of the MHD-LR model is 95%, with an error of only 0.95 mm. In addition, a method for detecting abnormal phenotyp ic defects in jujube was established. It can effectively and accurately classify seven kinds of jujube phenotypes (regular, irregular, wrinkled, moldy, hole-bro ken, skin-broken, and scarred). The data augmentation method based on linear int erpolation can effectively expand the dataset with a variance of only 0.0006. Su pport vector machine-decision tree (SVMDT), logistic regression, back propagatio n neural network, and long shortterm memory network models were established to classify jujube samples with different phenotypes, with accuracies of 99.57% , 99.00%, 99.14%, and 99.29%, respectivel y. The results showed that the SVMDT model had higher accuracy and explainabilit y."
NETL
NSTL