基于冠层光谱和覆盖度的马铃薯叶片钾含量估算方法

Estimation of Potassium Content in Potato Leaves Based on Canopy Spectrum and Coverage

叶片钾含量(Leaf potassium content, LKC)是表征作物钾素营养状况的重要指标,高效准确地获取马铃薯LKC有助于精准农业施肥管理。本研究旨在通过结合马铃薯关键生育期RGB影像提取的植被指数(VIs)和植被覆盖度(FVC),提高马铃薯关键生育期LKC估算的准确性。首先从马铃薯块茎形成期(S1)、块茎增长期(S2)和淀粉积累期(S3)的RGB影像中提取VIs和FVC,然后分别分析每个生育期VIs和FVC与马铃薯LKC的相关性,最后利用支持向量机(Support vector machine, SVM)、Lasso回归(Least absolute shrinkage and selection operator, Lasso)和岭回归构建马铃薯LKC的估算模型。结果表明:基于RGB影像提取的马铃薯FVC精度较高,且前两个生育期高于第3个生育期;利用VIs估算马铃薯LKC是可行的,但精度有待进一步提高;VIs结合FVC可以提高马铃薯LKC的估算精度。本研究可为作物生长和钾素营养状况监测提供技术参考。

Leaf potassium content(LKC)is an important indicator to characterize the potassium nutritional status of crops,and efficient and accurate acquisition of potato LKC can help precision agriculture fertilization management.The aim was to improve the accuracy of potato LKC estimation by combining vegetation indices(VIs)and vegetation cover(FVC)extracted from RGB images during the critical fertility period of potatoes.Firstly,VIs and FVC were extracted from the RGB images of potato tuber formation stage(S1),tuber growth stage(S2),and starch accumulation stage(S3).Then the correlation between VIs and FVC and potato LKC was analyzed for each fertility period separately.Finally,the correlation between VIs and FVC,and LKC was analyzed by using a support vector machine(SVM),least absolute shrinkage and selection operator regression(Lasso),and ridge regression used to construct the estimation model of potato LKC.The results showed that the accuracy of potato FVC extracted based on RGB images was high,and the first two fertility periods were higher than that of the third;the estimation of potato LKC using VIs was feasible,but the accuracy needed to be further improved;and the combination of VIs with FVC can improve the estimation accuracy of potato LKC.The research result can provide technical references for crop growth and potassium nutrient status monitoring.