基于叶绿素荧光光谱技术的茶叶藻斑病模型研究

茶叶是我国重要的经济作物，对茶叶病害的及早发现与诊断，有利于农业生产者及时采取有效的防护措施。为了实现对茶叶病害的准确判别，采用叶绿素荧光光谱对茶叶的光谱特性展开研究。实验采集了健康茶叶样本90片，藻斑病轻度病害叶片90片，藻斑病重度病害叶片90片，并根据Kennard-Stone算法将样本数按3∶1划分训练集和预测集样本数，其中校正集为200个、验证集为70个。采用叶绿素荧光光谱采集系统对茶叶藻斑病、正常叶片进行光谱采集，其中采集参数设置为：积分时间20 ms，激光功率40 mW。分别分析了患病叶片和正常叶片的光谱响应特性，总体上看，三种叶片光谱主要存在吸收强度差异，光谱走势基本一致。在685和740 nm附近存在叶绿素的荧光峰，其差异主要表现在正常叶片光谱较另外两种叶片光谱吸收强度较高，而重度病害强度最低。然后使用多项式平滑（Savitzky-Golay）对原始光谱进行平滑和降噪处理，建立了偏最小二乘判别模型（PLS-DA），在PLS-DA建模集模型中，误判样品数为3个，误判率为3%；PLS-DA预测集模型中，误判样品个数为5个，误判率为7.1%。然后建立4种不同核函数的支持向量机模型进行比较得到，由RBF作为核函数，经主成分分析法(PCA)降维后的变量建立的SVM模型误判率最低，准确率达到95.72%，最后采用PCA结合线性判别分析方法（LDA）建立的模型效果最好，准确率达到98.9%。其中最优主成分数的选取由留一验证法取得，选用前10个主成分进行建模时，交叉验证准确率最高，达98%。通过模型对比得到PLS-DA建模集和预测集精度都达到90%以上，以四种核函数建立的支持向量机模型中，径向基核函数模型效果较优，达到95.72%。经主成分分析后建立的LDA效果最好，识别率为98.9%。该研究采用叶绿素荧光光谱结合化学计量学对茶叶病害进行识别，为茶叶病害的快速、准确预测提供一种新方法。

Research on Tea Cephaleuros Virescens Kunze Model Based on Chlorophyll Fluorescence Spectroscopy

Tea is an important cash crop in China. The early detection and diagnosis of tea diseases will help agricultural producers to take effective protective measures in time. In order to achieve accurate discrimination of tea diseases, the spectral characteristics of tea were studied using chlorophyll fluorescence spectrum. A total of 90 samples of healthy tea leaves, 90 samples of the early stage of Cephaleuros virescens Kunze leaf disease and 90 samples of the severe stage of Cephaleuros virescens Kunze leaf disease were collected in the experiment and were accordance with the Kennard-Stone algorithm divided into the training set and prediction set according to the proportion of 3∶1 for each kind. Adopt the chlorophyll fluorescence spectrum collection system to collect the spectrum of tea leaf spot disease and normal leaves and set the collection parameters: integration time 20 ms and laser power 40 mW. The spectral response characteristics of the diseased and normal leaves were analyzed separately. In general, there are differences in the absorption intensity of the three types of leaves, and the spectrum trends are the same. There is a chlorophyll fluorescence peak near 685 and 740 nm. The difference is mainly reflected in the difference in fluorescence peak intensity. Then the polynomial smoothing(Savitzky-Golay)method was carried out for smoothing and noise reduction on the original spectral, the establishment of partial least squares discriminant model (PLS-DA), in the PLS-DA modeling set model, the number of misjudged samples is 3, the false positive rate is 3%; in the PLS-DA prediction set model, the number of false positive samples is 5, and the false positive rate is 7.1%. Then the support vector machine model established by 4 different kernel functions is compared. RBF is used as the kernel function. The SVM model established by PCA has the lowest misjudgment rate, and the accuracy rate reaches 95.72%. Finally, the model established by principal component analysis (PCA) and linear discriminant analysis (LDA) has the best effect, and the accuracy rate reaches 98.9%. The selection of the optimal number of principal components is obtained by the leave-one-out verification method. When the first 10 principal components are selected for modeling, the cross-validation accuracy rate is the highest, reaching 98%. Through model comparison, the accuracy of the PLS-DA modeling set and prediction set is more than 90%. Among the support vector machine models built with four kernel functions, the radial basis kernel function model is the best, reaching 95.72%, the linear discriminant model (LDA) established after principal component analysis has the best effect, and the recognition rate is 98.9%. This study uses chlorophyll fluorescence spectroscopy combined with chemometrics to identify tea diseases, providing a new method for rapid and accurate prediction of tea diseases.