摘要
[目的]建立各种品种菌草叶片叶绿素光谱模型为快速无损地评估菌草健康状态提供理论依据。[方法]利用多光谱相机拍摄光谱信息,使用RGB和HSV两种颜色空间系统进行NDSI、RSI植被指数指标的建立,并采用4种回归算法进行叶绿素相对含量即SPAD值和光谱信息建模,选出合适菌草的模型。[结果]不同品种的菌草SPAD值差异不显著,主要是健康和干枯叶片的差异。叶片SPAD值和光谱反射值具有良好的相关性,RGB颜色系统红波段是最相关的波段,相关性达到-0.79。试验证明红波段的变化在健康叶片和干枯叶片之间更敏感。2种颜色系统综合评价,建模精度及稳定由高到低依次为:包含5个通道的RGB颜色系统,HSV颜色系统,仅包含R、G、B 3个通道的颜色系统。4种反演方法中,反演效果最好依次为随机森林机器学习方法、支持向量机机器学习和逐步线性回归方法、一元线性统计方法。预测SPAD值拟合效果最好的是RGB-NDSI-RF类型,其拟合数R2为0.95、RMSE为3.04、MRE为0.19,验证R2为0.75、RMSE为8.26、MRE为0.76。[结论]机器学习方法尤其是随机森林适用于菌草建模,可以取得较高的精度,具有高适应和稳定性。预测SPAD值拟合效果最好的是RGBNDSI-RF类型,其模型可以适用于不同菌草品种的建模,敏感识别叶片的叶绿素含量以评估其健康状态。
【Objective】A model based on the leaf chlorophyll was established to provide a rapid,convenient,and non-invasive means for evaluating and predicting the quality of varieties of JUNCAOs.【Methods】Using a multispectral camera,spectra of leaf chlorophyll were obtained from different JUNCAOs.NDSI and RSI vegetation indices were established according to the RGB and HSV color space systems.A chlorophyll meter was used to obtain SPAD readings on JUNCAO leaves.Four regression algorithms were applied to build the model to correlate the spectral and SPAD measurements.【Results】A significant difference on SPAD was observed between healthy and dry or dead leaves,but not between different JUNCAO.A significant correlation was found between the leaf SPAD and spectral reflection,especially,the red band of RGB color showing a coefficient of−0.79.The high sensitivity of the red band in distinguishing healthy from withering leaves allowed an accurate and stable measurements by the color systems that ranked as:5-channel RGB>HSV>3-channel RGB.Among the 4 inversion methods,the rank was:random forest machine learning>support vector machine learning>stepwise linear regression>univariate linear statistical.And the best fitting function in predicting SPAD was the RGB-NDSI-RF type,which had a R2 of 0.95 vs.3.04 for RMSE and 0.19 for MRE and validation R2 of 0.75 vs.8.26 for RMSE and 0.76 for MRE.【Conclusion】As JUNCAO is becoming increasingly popular for wide applications,a reliable,rapid,non-invasive method to determine the plant quality was much desired.The current leaf chlorophyll spectra-based RGB-NDSI-RF model built by the random forest machine learning method demonstrated an applicability for evaluating varieties of JUNCAO.
作者
白妮妮
刘凤山
张厚喜
蔡杨星
卜建超
彭实梅
林冬梅
BAI Nini;LIU Fengshan;ZHANG Houxi;CAI Yangxing;BU Jianchao;PENG Shimei;LIN Dongmei(College of Forestry,Fujian Agriculture and Forestry University,Fuzhou,Fujian 350002,China;National Engineering Research Center of JUNCAO Technology,Fujian Agriculture and Forestry University,Fuzhou,Fujian 350002,China;College of Life Sciences,Fujian Agriculture and Forestry University,Fuzhou,Fujian 350002,China)
出处
《福建农业学报》
CAS
CSCD
北大核心
2023年第2期210-219,共10页
Fujian Journal of Agricultural Sciences
基金
国家自然科学基金项目(41801020)
国家菌草工程技术研究中心基金项目(CXZX206179)。
作者简介
白妮妮(1998−),女,硕士研究生,研究方向:无人机遥感与生态治理,E-mail:bainini0707@163.com;通信作者:刘凤山(1986−),男,博士,助理研究员,硕士研究生导师,研究方向:农业生态,E-mail:liufs.11b@igsnrr.ac.cn。
