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基于CARS-CNN的高光谱柑橘叶片含水率可视化研究
引用本文:代秋芳,廖臣龙,李 震,宋淑然,薛秀云,熊诗路. 基于CARS-CNN的高光谱柑橘叶片含水率可视化研究[J]. 光谱学与光谱分析, 2022, 42(9): 2848-2854. DOI: 10.3964/j.issn.1000-0593(2022)09-2848-07
作者姓名:代秋芳  廖臣龙  李 震  宋淑然  薛秀云  熊诗路
作者单位:华南农业大学电子工程学院(人工智能学院) ,广东 广州 510642;国家柑橘产业技术体系机械化研究室 ,广东 广州 510642;广东省农情信息监测工程技术研究中心 ,广东 广州 510642;华南农业大学电子工程学院(人工智能学院) ,广东 广州 510642;国家柑橘产业技术体系机械化研究室 ,广东 广州 510642
基金项目:国家自然科学基金项目(31971797),广东省现代农业产业技术体系创新团队建设专项资金项目(2021KJ108),财政部和农业农村部:国家现代农业产业技术体系项目(CARS-26)资助
摘    要:柑橘叶片水分亏缺是影响柑橘生长发育的重要因素之一,为研究水分胁迫对柑橘含水率的影响,利用高光谱快速无损检测柑橘叶片含水率,并应用伪彩色处理实现含水率可视化。收集100片柑橘叶片,使用烘干法得到鲜叶和烘干叶片一共500个不同梯度含水率的数据样本,将样本按7∶3的比例划分为训练集(350个样本)和测试集(150个样本),使用决定系数(R2)和均方根误差(RMSE)来评估模型预测的好坏。采用卷积神经网络(CNN)对高光谱数据进行预测,CNN模型使用一维卷积核,一共三层卷积池化层,使用RELU激活函数激活,输出层采用linear激活函数回归预测,使用nadam算法对模型进行优化更新,迭代次数为1 000次;将原始光谱数据和SG,MSC和SNV三种预处理后的光谱数据,与全波段、CARS筛选的特征波段、PCA提取的特征波段组合,导入CNN模型,确定最佳模型为原始光谱数据的CARS-CNN,训练集的R2c和RMSEC分别为0.967 9和0.016 3,测试集的R2v和RMSEV分别为0.9...

关 键 词:柑橘叶片  含水率  高光谱  卷积神经网络  可视化
收稿时间:2021-07-28

Hyperspectral Visualization of Citrus Leaf Moisture Content Based on CARS-CNN
DAI Qiu-fang,LIAO Chen-long,LI Zhen,SONG Shu-ran,XUE Xiu-yun,XIONG Shi-lu. Hyperspectral Visualization of Citrus Leaf Moisture Content Based on CARS-CNN[J]. Spectroscopy and Spectral Analysis, 2022, 42(9): 2848-2854. DOI: 10.3964/j.issn.1000-0593(2022)09-2848-07
Authors:DAI Qiu-fang  LIAO Chen-long  LI Zhen  SONG Shu-ran  XUE Xiu-yun  XIONG Shi-lu
Affiliation:1. College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University,Guangzhou 510642, China2. Division of Citrus Machinery, China Agriculture Research System, Guangzhou 510642, China3. Information Monitoring Engineering Technology Research Center, Guangzhou 510642, China
Abstract:Water deficit of citrus leaves is one of the important factors affecting the growth of citrus. In order to study the effect of water stress on the moisture content of citrus, hyperspectral technology was used to rapidly and non-destructively detect the moisture content of citrus leaves, and pseudo-color processing was applied to realize the visualization of moisture content. 100 citrus leaves were collected, and 500 leaves with different gradient moisture content were obtained by drying method. The samples were divided into a training set (350 samples) and a testing set (150 samples) according to the ratio of 7∶3. The determination coefficient (R2) and root mean square error (RMSE) was used to evaluate the model’s prediction quality. A convolution neural network (CNN) is used to predict spectrum data. The CNN model uses a one-dimensional convolution kernel with three convolution pooling layers activated by the RELU activation function. The output layer uses a linear activation function for regression prediction, and the nadam algorithm is used to optimize and update the model with 1 000 epochs; The Raw spectrum data and the spectrum data are pretreated by SG, MSC and SNV are used respectively. The full bands, the feature bands screened by CARS and the feature bands extracted by PCA are imported into the CNN model respectively. The best model is CARS-CNN of the Raw spectrum data, the R2c and RMSEC of the training set are 0.967 9 and 0.016 3 respectively. The R2v and RMSEV of the testing set are 0.947 0 and 0.021 4, respectively. The effect of the full bands CNN model of the Raw spectrum data is the second, and the R2c and RMSEC of the training set are 0.934 3 and 0.024 9, respectively. The R2v and RMSEV of the testing set are 0.915 9 and 0.028 6, respectively; At the same time, the best combined results of the support vector machine regression model (SVR), partial least squares regression model (PLSR) and random forest model (RF) under different pretreatment methods and characteristic wavelength selection were compared. The best combination model (Raw spectrum+CARS+PLSR, SNV+PCA+RF, SNV+PCA+SVR) was compared with CARS-CNN of Raw spectrum data, CARS-CNN model still has the best prediction effect. Compared with other models, the CARS-CNN model has higher prediction accuracy than SVR, PLSR and RF models, after further feature extraction by CARS and convolution kernel. Select the trained CARS-CNN model, import the hyperspectral image into the model, calculate the moisture content of each pixel, and get the pseudo-color image, which can more intuitively display the visual distribution of leaf moisture content. The result provides a faster, more intuitive and more comprehensive assessment of citrus leaf moisture content, a basis for the study of citrus leaf water stress, and a reference for optimising intelligent irrigation decision-making.
Keywords:Citrus leaf  Moisture content  Hyperspectral  Convolution neural network  Visualization  
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