Nitrogen balance index (NBI) is an important indicator for scientific diagnostic and quantitative research on crop growth status. The quick and accurate assessment of NBI is necessary for farmers to make timely N management decisions. The objective of the study was to estimate winter wheat NBI based on canopy hyperspectral features between 400–1350 nm combined with machine learning (ML) methods in the individual and whole growth stages. In this study, 3 years of winter wheat plot experiments were conducted. Ground-level canopy hyperspectral reflectance and corresponding plant NBI values were measured during the jointing, booting, flowering and filling stages. Continuous removal spectra (CRS) and logarithmic transformation spectra (LOGS) were derived from the original canopy spectra. Sensitive bands and vegetation indices (VIs) highly correlated with NBI under different spectral transformations were selected as hyperspectral features to construct the NBI estimation models combined with ML algorithms. The study indicated that the spectral transformation significantly improved the correlation between the sensitive bands, VIs and the NBI. The correlation coefficient of the sensitive band in CRS in the booting stage increased by 27.87%, reaching −0.78. The leaf chlorophyll index (LCI) in LOGS had the highest correlation with NBI in the filling stage, reaching a correlation coefficient of −0.96. The NBI prediction accuracies based on the sensitive band combined with VIs were generally better than those based on the univariate hyperspectral feature, and the prediction accuracy of each growth stage was better than that of the whole growth stage. The random forest regression (RFR) method performed better than the support vector regression (SVR) and partial least squares regression (PLS) methods. The NBI estimation model based on the LOGS-RFR method in the filling stage could explain 95% of the NBI variability with relative prediction deviation (RPD) being 3.69. These results will provide a scientific basis for better nitrogen nutrition monitoring, diagnosis, and later for field management of winter wheat.

Canopy chlorophyll content (CCC) is closely related to crop nitrogen status, crop growth and productivity, detection of diseases and pests, and final yield. Thus, accurate monitoring of chlorophyll content in crops is of great significance for decision support in precision agriculture. In this study, winter wheat in the Guanzhong Plain area of the Shaanxi Province, China, was selected as the research subject to explore the feasibility of canopy spectral transformation (CST) combined with a machine learning method to estimate CCC. A hyperspectral canopy ground dataset in situ was measured to construct CCC prediction models for winter wheat over three growth seasons from 2014 to 2017. Sensitive-band reflectance (SR) and narrow-band spectral index (NSI) were established based on the original spectrum (OS) and CSTs, including the first derivative spectrum (FDS) and continuum removal spectrum (CRS). Winter wheat CCC estimation models were constructed using univariate regression, partial least squares (PLS) regression, and random forest (RF) regression based on SR and NSI. The results demonstrated the reliability of CST combined with the machine learning method to estimate winter wheat CCC. First, compared with OS-SR (683 nm), FDS-SR (630 nm) and CRS-SR (699 nm) had a larger correlation coefficient between canopy reflectance and CCC; secondly, among the parametric regression methods, the univariate regression method with CRS-NDSI as the independent variable achieved satisfactory results in estimating the CCC of winter wheat; thirdly, as a machine learning regression method, RF regression combined with multiple independent variables had the best winter wheat CCC estimation accuracy (the determination coefficient of the validation set (R_v²) was 0.88, the RMSE of the validation set (RMSE_v) was 3.35 and relative prediction deviation (RPD) was 2.88). Thus, this modeling method could be used as a basic method to predict the CCC of winter wheat in the Guanzhong Plain area.

The sweetness is an important parameter for the quality of Cuixiang kiwifruit. The quick and accurate assessment of sweetness is necessary for farmers to make timely orchard management and for consumers to make purchasing choices. The objective of the study was to propose an effective physical method for determining the sweetness of fresh kiwifruit based on fruit hyperspectral reflectance in 400–2500 nm. In this study, the visible and near-infrared spectral (Vis/NIR) reflectance and sweetness values of kiwifruit were measured at different time periods after the fruit matured in 2021 and 2022. The multiplicative scatter correction (MSC) and standard normal variable (SNV) transformation were used for spectral denoising. The successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) methods were employed to select the most effective features for sweetness, and then the features were used as the inputs of partial least squares (PLS), least squares support vector machine (LSSVM), back propagation neural network (BP), and multiple linear regression (MLR) models to explore the best way of sweetness predicting. The study indicated that the most sensitive features were in the blue and red regions and the 970, 1200, and 1400 nm. The sweetness estimation model constructed by using the data of the whole harvest period from August to October performed better than the models constructed by each harvest period. Overall results indicated that hyperspectral reflectance incorporated with MSC-SPA-LSSVM could explain up to 79% of the variability in kiwifruit sweetness, which could be applied as an alternative fast and accurate method for the non-destructive determination of the sweetness of kiwifruit. This research could partially provide a theoretical basis for the development of nondestructive instrumentation for the detection of kiwifruit sweetness.