首页|Hubei Normal University Researchers Update Current Data on Machine Learning (Acc urate leaf area index estimation for Eucalyptus grandis using machine learning m ethod with GF-6 WFV-A case study for Huangmian town, China)
Hubei Normal University Researchers Update Current Data on Machine Learning (Acc urate leaf area index estimation for Eucalyptus grandis using machine learning m ethod with GF-6 WFV-A case study for Huangmian town, China)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on artificial intelligenc e is the subject of a new report. According to news reporting out of Huangshi, P eople's Republic of China, by NewsRx editors, research stated, "Spectral and tex ture features play important roles in plantation leaf area index (LAI) estimatio n, and their combination may enhance LAI inversion accuracy. Furthermore, resear ch on the impact of different machine learning (ML) models on their hyperparamet er combinations and splitting ratios remains challenging." Our news reporters obtained a quote from the research from Hubei Normal Universi ty: "In our study, experiments based on spectral and textural features of GF-6 W FV data were conducted on Eucalyptus grandis plantation forests in Huangmian Tow n, Guangxi, China. ML methods such as multiple stepwise regression (MSR), random forest (RF), back-propagation neural network (BPNN), and support vector regress ion (SVR) were mainly utilized to perform model hyper-parameter tuning and split -ratio analysis in order to estimate the LAI. The results of the study showed th at spectral and gray level co-occurrence matrix (GLCM) texture features were ver y sensitive to changes in Eucalyptus grandis LAI. The accuracy of combining the two was 10% higher than when they were not combined. Furthermore, it was found that the nonlinear methods (RF, BPNN, and SVR) outperformed the lin ear method (MSR), with the average Rmax2 of the nonlinear model being 26% higher than that of the linear model, and the RMSE value being 29% lower than that of the linear model. In addition, by analyzing different combina tions of features, model hyperparameter fine-tuning, and splitting ratios in the nonlinear model, it was found that the splitting ratios of different combinatio ns of model hyperparameters have a great impact on the accuracy of the model. A total of 12 out of 21 data sets showed high accuracy and stability at a split ra tio of 8.5:1.5 (ratio of 0.85), with the best-performing RF model differing from the lowest by 91% for Rmax2 and 39% for Rstd2."
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