文献类型：期刊文献

英文题名：Classification of Toona sinensis Young Leaves Using Machine Learning and UAV-Borne Hyperspectral Imagery

作者：Wu, Haoran[1,2] Song, Zhaoying[1,2] Niu, Xiaoyun[1] Liu, Jun[2] Jiang, Jingmin[2] Li, Yanjie[2]

第一作者：Wu, Haoran

通信作者：Liu, J[1];Li, YJ[1]

机构：[1]Hebei Agr Univ, Coll Landscape Architecture & Tourism, Baoding, Peoples R China;[2]Chinese Acad Forestry, Res Inst Subtrop Forestry, Hangzhou, Peoples R China

年份：2022

卷号：13

外文期刊名：FRONTIERS IN PLANT SCIENCE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000824161700001)】；

基金：Funding This work was funded by the Zhejiang Science and Technology Major Program on Agricultural (forest) New Variety Breeding (2021C02070-1-1).

语种：英文

外文关键词：machine learning; preprocessing; classification; Toona sinensis young leaves; variable selection

摘要：Rapid and accurate distinction between young and old leaves of Toona sinensis in the wild is of great significance to the selection of T. sinensis varieties and the evaluation of relative yield. In this study, UAV hyperspectral imaging technology was used to obtain canopy hyperspectral data of biennial seedlings of different varieties of T. sinensis to distinguish young and old leaves. Five classification models were trained, namely Random Forest (RF), Artificial Neural Network (ANN), Decision Tree (DT), Partial Least Squares Discriminant Analysis (PLSDA), and Support Vector Machine (SVM). Raw spectra and six preprocessing methods were used to fit the best classification model. Satisfactory accuracy was obtained from all the five models using the raw spectra. The SVM model showed good performance on raw spectra and all preprocessing methods, and yielded higher accuracy, sensitivity, precision, and specificity than other models. In the end, the SVM model based on the raw spectra produced the most reliable and robust prediction results (99.62% accuracy and 99.23% sensitivity on the validation set only, and 100.00% for the rest). Three important spectral regions of 422.7~503.2, 549.2, and 646.2~687.2 nm were found to be highly correlated with the identification of young leaves of T. sinensis. In this study, a fast and effective method for identifying young leaves of T. sinensis was found, which provided a reference for the rapid identification of young leaves of T. sinensis in the wild.