Li Yumeng, Wang Chunying, Zhu Junke, Wang Qinglong, Liu Ping
Shandong Engineering Research Center of Agricultural Equipment Intelligentization, Shandong Key Laboratory of Intelligent Production Technology and Equipment for Facility Horticulture, College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an 271018, China.
State Key Laboratory of Wheat Improvement, Shandong Agricultural University, Tai'an 271018, China.
Plants (Basel). 2025 Jun 20;14(13):1908. doi: 10.3390/plants14131908.
Aiming at tackling the challenges of traditional classification methods, which are labor-intensive, time-consuming, and inefficient, a nitrogen-efficient wheat variety classification method using support vector machine-extreme gradient boosting (SVM-XGBoost) based on unmanned aerial vehicle (UAV) hyperspectral remote sensing was proposed in this study. First, eight agronomic indicators closely related to wheat nitrogen efficiency were analyzed using t-SNE dimensionality reduction and hierarchical clustering, enabling the classification of 12 wheat varieties into nitrogen-efficient and nitrogen-inefficient varieties under different nitrogen stress conditions. Second, a hyperspectral feature band selection method based on least absolute shrinkage and selection operator-competitive adaptive reweighted sampling (Lasso-CARS) was employed using hyperspectral canopy data collected during the wheat heading stage with an UAV to extract feature bands relevant to nitrogen-efficient wheat classification. This approach aimed to mitigate the impact of high collinearity and noise in high-dimensional hyperspectral data on model construction. Furthermore, the SVM-XGBoost method integrated the extracted feature bands with the support vectors and decision function outputs from the preliminary SVM classification. It then leveraged XGBoost to capture nonlinear relationships and construct the final classification model using gradient-boosted trees, achieving intelligent classification of nitrogen-efficient wheat varieties. The model also selected nitrogen fertilization strategies based on the characteristics of different wheat varieties. The results demonstrated robust performance under low, high, and no nitrogen stress, with average overall accuracies of 74%, 83%, and 70% (Kappa coefficients: 0.67, 0.80, and 0.48), respectively. This study provided an efficient and accurate UAV hyperspectral remote sensing-based method for nitrogen-efficient wheat variety classification, offering a technological foundation to accelerate precision breeding.
针对传统分类方法存在的劳动强度大、耗时且效率低下的问题，本研究提出了一种基于无人机（UAV）高光谱遥感的支持向量机-极限梯度提升（SVM-XGBoost）小麦氮效率品种分类方法。首先，使用t-SNE降维和层次聚类分析了与小麦氮效率密切相关的8个农艺指标，从而能够在不同氮胁迫条件下将12个小麦品种分为氮高效和氮低效品种。其次，利用无人机在小麦抽穗期收集的冠层高光谱数据，采用基于最小绝对收缩和选择算子-竞争自适应重加权采样（Lasso-CARS）的高光谱特征波段选择方法，提取与氮高效小麦分类相关的特征波段。该方法旨在减轻高维高光谱数据中的高共线性和噪声对模型构建的影响。此外，SVM-XGBoost方法将提取的特征波段与初步SVM分类的支持向量和决策函数输出相结合。然后利用XGBoost捕捉非线性关系，并使用梯度提升树构建最终分类模型，实现氮高效小麦品种的智能分类。该模型还根据不同小麦品种的特性选择氮肥施用策略。结果表明，该模型在低氮、高氮和无氮胁迫下均表现出稳健的性能，平均总体准确率分别为74%、83%和70%（Kappa系数：0.67、0.80和0.48）。本研究为氮高效小麦品种分类提供了一种基于无人机高光谱遥感的高效准确方法，为加速精准育种提供了技术基础。
