辣椒叶片水分胁迫高光谱特性研究

Spectral Characteristics of Water Stress in Chili Pepper Leaves

摘要:

为精准判别作物需水程度，以生长期辣椒为实验样本，对辣椒进行不同程度的水浸、干旱等水分胁迫处理，分析不同水分胁迫程度下辣椒叶片的高光谱响应特性。样本分为重度干旱、轻度干旱、轻度水浸、重度水浸等4个水分胁迫组和一个实验对照组，共5个数据组，每组20株辣椒，待各组叶片外观出现明显差异时，分别采集各组辣椒叶片的叶绿素荧光参数与高光谱数据。比较多元散射校正（MSC）、SG卷积平滑滤波和标准正态变换（SNV）3种不同的预处理方法对背景信息干扰的消除效果。采用SPA算法和CARS算法提取对水分胁迫敏感的特征波长。建立预测辣椒叶片不同水分胁迫程度的支持向量机（SVM）、BP神经网络、径向基函数（RBF）和随机森林（RF）模型。结果说明，SG-SPA-RFB为预测辣椒叶片水分胁迫程度的最优组合，其训练集准确率为99.02%，测试集准确率为94.00%。本研究为判断辣椒植株水分胁迫状态提供了一种便捷可靠的无损检测方法。

关键词:辣椒;水分胁迫;高光谱图像;特征波长;神经网络;叶绿素荧光参数

Abstract:

In response to the need for smart agriculture to accurately discriminate the degree of crop water demand, taking growing peppers as the experimental samples, different degrees of water stress treatments such as water immersion and drought to the leaves of peppers were applied to analyze the hyperspectral response characteristics of pepper leaves under different degrees of water stress. The samples were divided into four water stress groups, including severe drought, mild drought, mild water-soaked, and severe water-soaked, and one experimental control group, with a total of five data groups of 20 chili peppers in each group, and the chlorophyll fluorescence parameters and hyperspectral data of chili peppers’ leaves in each group were collected separately when the appearance of leaves in each group appeared to be obviously different. The effects of three different preprocessing methods, namely, multiplicative scatter correction (MSC), SG convolutional smoothing filter and standard normal variate transform (SNV), on the elimination of background information interference were compared. The SPA algorithm and CARS algorithm were used to extract the characteristic wavelengths sensitive to water stress. Support vector machine (SVM), BP neural network, radial basis function (RBF) and random forest (RF) modeling were established for predicting different levels of water stress. The results illustrated that SG-SPA-RFB was the optimal combination for predicting the degree of water stress with 99.02% accuracy in the training set and 94.00% accuracy in the test set. The research result can provide a convenient and reliable non-destructive method for determining the water stress status of pepper plants.

Key words:pepper;water stress;hyperspectral image;characteristic wavelength;neural network;chlorophyll fluorescence parameters