An efficient method to reduce grain angle influence on NIR spectra for predicting extractives content from heartwood stem cores of Toona. sinensis

Li, Yanjie; Dong, Xin; Sun, Yang; Liu, Jun; Jiang, Jingmin

doi:10.1186/s13007-020-00623-3

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Moreover, evaluating the yield of T. sinensis by picking is not applicable in a large area. Among the spectroscopic studies of T. sinensis , most studies focus on the estimation of leaf biochemistry (e.g., chlorophyll and nitrogen content) and wood chemicals (e.g., heartwood extract content; Li et al, 2020 ; Liu et al, 2021b ). However, few studies discuss how to quickly distinguish young and old leaves of T. sinensis, which plays an important role in breeding and estimating relative T. sinensis yield and provides a technical basis for machine picking of T. sinensis .…”

Section: Discussionmentioning

confidence: 99%

Classification of Toona sinensis Young Leaves Using Machine Learning and UAV-Borne Hyperspectral Imagery

Song

Niu

et al. 2022

Front. Plant Sci.

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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.

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Section: Discussionmentioning

confidence: 99%