基于Adaboost及高光谱的生菜叶片氮素水平鉴别研究

为了便于经济合理的生菜施肥，研究一种生菜叶片氮素水平智能鉴别方法。在温室大棚内无土栽培不同氮素水平的生菜样本，在特定生育期，采集各类氮素水平生菜样本，利用FieldSpec○R 3型光谱仪采集生菜叶片高光谱数据。由于原始高光谱数据存在噪声且冗余性强，利用标准归一化(SNV)对原始高光谱数据进行降噪处理，再利用主成分分析方法(PCA)对高光谱数据进行特征提取。分别利用K最近邻(KNN)和支持向量机(SVM)对降维后的光谱数据进行分类研究，由于自适应提升法(Adaboost)能提升弱分类器分类性能，将其分别引入到KNN和SVM两种分类器中，提出了Adaboost-KNN和Adaboost-SVM两种集成分类算法。分别利用上述四种分类算法对相同测试样本数据进行分类鉴别。结果表明，KNN，SVM，Adaboost-KNN和Adaboost-SVM四种算法的分类正确率分别为74.68%，87.34%，100%和100%，其中所提出的Adaboost-KNN与Adaboost-SVM分类效果都很好，且Adaboost-SVM分类算法的稳定性最好。因此，Adaboost-SVM算法适合作为基于高光谱的生菜氮素水平鉴别的建模方法，并且也为其他作物营养元素无损检测提供了一种新的方法。

Identification of Lettuce Leaf Nitrogen Level Based on Adaboost and Hyperspectrum

In order to facilitate lettuce fertilization in an economically rational way, an intelligent method to identify lettuce leaf nitrogen levels was studied. Lettuce samples of different nitrogen levels were cultivated in greenhouse with soilless cultivation method. In a particular growth period, the lettuce samples in various nitrogen levels were collected, then the FieldSpec○R3 spectrometer was used to acquire the hyperspectral data of the cultivated lettuce leaves. As there were much noise and redundant information in original hyperspectral data, standard normal variate transformation (SNV) was used to reduce the noise of the original hyperspectral data in this paper, then the principal component waves were extracted by principal component analysis (PCA). While K nearest neighbor (KNN) and support vector machine (SVM) were used for classification studies on the processed hyperspectra data respectively, adaptive boosting (Adaboost) was introduced into the two classifiers as it could improve the classification performance of weak classifiers, then Adaboost-KNN and Adaboost-SVM, the two integrated classification algorithms, were proposed. At last, the four classification algorithms were used for classification and identification of the same test sample data respectively, with the results showing that the classification accuracies of KNN, SVM, Adaboost-KNN and Adaboost-SVM were high up to 74.68%, 87.34%, 100% and 100%, among which the classification accuracies of Adaboost-KNN and Adaboost-SVM proposed in this paper were both good, and the stability of Adaboost-SVM was the best. Therefore, Adaboost-SVM used as a modeling method is suitable for the identification of lettuce leaf nitrogen level based on hyperspectrum, and it can also be used for reference to identify the nutrient elements of other crops in nondestructive testing methods.