首页|China Agricultural University Reports Findings in Machine Learning (Abnormal phe notypic defects detection of jujube using explainable machine learning enhanced computer vision)
China Agricultural University Reports Findings in Machine Learning (Abnormal phe notypic defects detection of jujube using explainable machine learning enhanced computer vision)
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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."
BeijingPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine Learning
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