Hyperspectral estimation of maize (Zea mays L.) yield loss under lodging stress

Abstract

The frequency and intensity of maize (Zea mays L.) yield disturbance caused by lodging stress are getting higher and higher, so it is of great significance to take effective methods to monitor the yield loss of lodging maize. This study aimed to explore the ability of hyperspectral technology to estimate maize yield loss under lodging. The lodging control experiment of maize was carried out, the changes of canopy hyperspectral and per unit yield loss of maize in multiple growth stages were analyzed. First, the successive projections algorithm (SPA) and recursive feature elimination (RFE) methods were used to select yield-sensitive wavelengths from original canopy spectrum (OCS) to achieve dimensionality reduction. Then, the fractional-order differential (FOD) transform was applied for the canopy spectrum, and the RFE method was used to select the optimal wavelength combination. Finally, the models of estimating per unit yield of maize with different lodging periods and days after lodging (DAL) were constructed by using the optimal wavelength combination, and the model was verified by leave-one-out cross-validation (LOOCV) method. It was found that the more serious the lodging severity, the greater the maize per unit yield loss. On 1, 7, 14, 21 DAL at vegetative tasseling (VT) stage and 1, 7 DAL at reproductive milk (R3) stage, the model accuracy of RFE method was 5.26%, 17.39%, 9.46%, 6.41%, 20.37% and 11.11% higher than that of SPA method. The estimation accuracy of lodging maize yield model was improved by using FOD method and RFE method (FOD-RFE). The R2 of the maize yield model of 1.4-order, 0.9-order, 1.8-order, 0.6-order on 1, 7, 14 and 21 DAL at VT stage reached 0.90, 0.89, 0.92, 0.93, which were 12.5%, 11.11%, 13.58% and 12.05% higher than OCS-RFE. The R2 of the maize yield model of 1.5-order and 1.5-order on 1 and 7 DAL at R3 stage were 0.84 and 0.87, which were 29.23% and 24.29% higher than OCS-RFE. Therefore, fractional-order differential transform and spectral feature selection could improve the estimation accuracy of maize yield under lodging stress. The hyperspectral technology could be used to quickly estimate maize yield loss under lodging stress.