基于机器学习的马铃薯叶片叶绿素含量估算

Estimation of Chlorophyll Content in Potato Leaves Based on Machine Learning

为了提高马铃薯叶绿素含量估算模型的精度,使用无人机平台搭载多光谱相机,获取对照处理和干旱处理下马铃薯关键生育期的遥感影像,选取13种植被指数作为叶绿素含量反演模型的输入变量,使用多元线性回归(MLR)、支持向量回归(SVR)、随机森林回归(RFR)、决策树回归(DTR)构建马铃薯叶绿素含量估算模型。首先分析了植被指数与叶绿素含量之间的相关性,结果表明,在对照处理块茎形成期,CIre、GNDVI、NDVIre、NDWI、GRVI、LCI与叶绿素含量之间的相关系数绝对值在0.5以上,且存在显著(p<0.05)或极显著(p<0.01)相关性;在马铃薯其他生育时期,13种植被指数与叶绿素含量之间的相关系数绝对值均在0.5以上,且存在极显著(p<0.001)相关性。然后对MLR、SVR、RFR和DTR等模型的精度进行比较,结果表明:SVR模型在对照处理块茎形成期、块茎膨大期和淀粉积累期的预测效果均是最佳,R 2和RMSE在块茎形成期为0.89和2.11,块茎膨大期为0.59和4.03,淀粉积累期为0.80和3.18;RFR模型在干旱处理块茎形成期、块茎膨大期和淀粉积累期的预测效果均是最佳,R 2和RMSE在块茎形成期为0.90和1.57,在块茎膨大期为0.87和2.16,在淀粉积累期为0.63和3.01。该研究为马铃薯叶绿素含量监测提供一种新的方法,后期可根据不同试验处理选择相应的估算模型。

To improve the accuracy of the photo chlorophyll content estimation model,the remote sensing images of different growth stages of potatoes under control and drought treatments were obtained using a multi-spectral camera on a UAV platform.Thirteen vegetation indices were selected as input variables of the chlorophyll content inversion model,and the estimation model of potato chlorophyll content was constructed by using multiple linear regression(MLR),support vector regression(SVR),random forest regression(RFR)and decision tree regression(DTR).Correlation analysis between vegetation index and chlorophyll content showed that at the tuber formation stage of the control treatment,the absolute values of correlation coefficients between CIre,GNDVI,NDVIre,NDWI,GRVI,LCI and chlorophyll content were above 0.5,and their were significant(p<0.05)or highly significant(p<0.01)correlations.In other growth stages of potato,the absolute values of correlation coefficients between 13 vegetation indexes and chlorophyll content were all above 0.5,which was a highly significant correlation(p<0.001).In addition,the accuracy of MLR,SVR,RFR and DTR models were compared.The results showed that the SVR model has the best prediction effects in the tuber formation stage,tuber expansion stage and starch accumulation stage of the control treatment.The control treatment s R 2 and RMSE were 0.89 and 2.11 in the tuber formation stage,0.59 and 4.03 in the tuber expansion stage,and 0.80 and 3.18 in the starch accumulation stage.The RFR model produces the best prediction effects in the tuber formation,tuber expansion,and starch accumulation stages of the drought treatment.The outcomes of R 2 and RMSE on drought treatment were 0.90 and 1.57 in the tuber formation stage,0.87 and 2.16 in the tuber expansion stage,and 0.63 and 3.01 in the starch accumulation stage.This study presents a new approach for monitoring the chlorophyll content of potatoes,and a corresponding estimating model can be selected based on the specific potato growth stage and different experimental treatments in future.