Study on Maize Leaf Nitrogen Inversion Model Based on Equivalent Water Thickness Gradient

According to the actual need for rapid and non-destructive testing methods for nitrogen in maize production. Samples were divided according to the equivalent water thickness gradient, and a gradient continuous leaf nitrogen inversion model was established. The influence of water content on leaf reflectance characteristics and the accuracy of the inversion model is preliminarily explored. Firstly, the hyperspectral data of leaf-level are obtained, and then the samples are sorted and sliding divided according to the value of equivalent water thickness, and the subset set is established. In addition to the original spectral data, the parent set also adopts (1) baseline correction; (2) Scattering correction; (3) Smoothing methods, three categories of spectral transformation methods, while subsets do not use any spectral transformation techniques. A full band PLSR inversion model is established, the model accuracy is compared, and the influence of equivalent water thickness on modeling accuracy is preliminarily quantitatively evaluated. The experimental results show that: (1) among the four groups of data, the inversion accuracy of three parent sets is lower than that of the optimal subset, and the other group is the same (2018 field-N: (parent set) R6 =0. 48<(subset) R. = 0. 57, RPDev =1. 38<RPDev =1. 52; 2018 field -J N: R6 =0. 48<ky =0. 7, RPDev =1. 39 <RPDev =1. 8; 2019 field -JN: R6 =0. 59<ky =0. 68, RPDev =1. 57<RPDe(v) =1. 77); (2) The inversion accuracy of the optimal subset of all the four groups reaches or even exceeds the level of the qualitative model, while the parent set has only two groups; (3) In the problem of sample selection of inversion data set, the factor of equivalent water thickness needs to be fully considered to avoid the loss of overall inversion accuracy caused by too wide sample selection. In conclusion, the factor of equivalent water thickness significantly impacts the accuracy of nitrogen modeling in maize leaves, which should not be ignored. After this factor is considered, the method of rapid, nondestructive detection of nitrogen in maize leaves using leaf hyperspectral data will be more reliable and feasible.