The chlorophyll fluorescence parameter Fv/Fm (maximum photosynthetic efficiency of optical system II) is an intrinsic index for exploring plant photosynthesis. Hyperspectral remote sensing technology can be used for rapid nondestructive detection of chlorophyll fluorescence parameters. Existing studies show that there is a good correlation between the vegetation index and Fv/Fm. However, due to the limited hyperspectral information reflected by the vegetation index, the established model often cannot reach the ideal accuracy. Therefore, this study took rice as the research object and explored the internal relationship between chlorophyll fluorescence parameters and spectral reflectance by setting different fertilization treatments. Spectral sensitive information was extracted by vegetation index and continuous wavelet transform (CWT) to explore a more suitable method for Fv/Fm hyperspectral estimation at the rice leaf scale. Then a monitoring model of Fv/Fm in rice leaves was established by the back propagation neural network (BPNN) algorithm. The results showed that: (1) the accuracy of univariate models constructed by Fv/Fm inversion based on 10 commonly used vegetation indices constructed by traditional methods was low; (2) The correlation between leaf hyperspectral reflectance and Fv/Fm could be effectively improved by using CWT, and the accuracy of the univariate model constructed by using the best wavelet coefficients could reach the level of rough evaluation of Fv/Fm; (3) The effect of wavelet transform using different mother wavelet functions as the basis function was different, and bior3.3 function was the best; R2, RMSE and RPD of the BPNN model constructed by using the first 10 best wavelet coefficients decomposed by the bior3.3 was 0.823 6, 0.013 2 and 2.304 3. In conclusion, this study proves that CWT can effectively extract sensitive bands of rice leaves for Fv/Fm monitoring, providing a reference for the follow-up rapid and nondestructive monitoring of chlorophyll fluorescence.

In this study, we investigated how printed sowing machine transplanting impacts the yield of single-season rice by increasing the planting density and decreasing the amount of fertilizer needed. The study was aimed at exploring the relationships between the amount of fertilizer, transplanting density, and rice yield. During the rice growing season from 2019 to 2020 in the middle and lower reaches of the Yangtze River, six different field trials were conducted: low density and high fertilizer (LDHF), low density and low fertilizer (LDLF), middle density and high fertilizer (MDHF), middle density and low fertilizer (MDLF), high density and high fertilizer (HDHF), and high density and low fertilizer (HDLF). It turns out that compared to the LDHF, the thousand seed weight, the spikelets per panicle, the seed-setting rate, and the SPAD value at the filling stage decreased by 0.17% and 0.60%, 5.36% and 10.59%, 5.70% and 4.66%, and 17.52% and 4.93% in 2019 and 2020, respectively. However, compared to the LDHF, the panicles increased by 15.31% and 17.18%, respectively, the LAI at the filling stage increased by 1.92% and 0.48%, respectively, and the accumulation of dry matter above ground at the maturity stage also increased by 3.74% and 16.79% in 2019 and 2020, respectively. Therefore, compared to the yield of rice in the LDHF, the yield of rice in the HDLF increased by 5.06% and 6.64%. The yields of rice in the LDLF, MDHF, MDLF, and HDHF were lower than that in the LDHF and HDLF. The partial least squares path model (PLSPM) analysis showed that the fertilizer, density, and aboveground dry matter had positive effects on the yield, while the SPAD value and LAI had negative effects on the yield. This research shows that increasing the transplanting density can compensate for the yield loss caused by reducing the fertilizer amount. However, no combination of the transplanting density and fertilization amount can achieve the purpose of increasing the yield.

The chlorophyll fluorescence parameter Fv/Fm plays a significant role in indicating the photosynthetic function of plants. The existing technical methods used to measure Fv/Fm are often inefficient and cumbersome. To realize fast and non-destructive monitoring of Fv/Fm, this study took rice under different fertilizer treatments and measured the hyperspectral reflectance information and Fv/Fm data of rice leaves during the whole growth period. Five spectral transformation methods were used to pre-process the spectral data. Then, spectral characteristic wavelengths were extracted by the correlation coefficient method (CC) combined with the competitive adaptative reweighted sampling (CARS) algorithm. Finally, based on the combination of characteristic wavelengths extracted from different spectral transformations, back propagation neural network (BPNN) models were constructed and evaluated. The results showed that: (1) first derivative transform (FD), multiplicative scatter correction (MSC) and standardized normal variation (SNV) methods could effectively highlight the correlation between spectral data and Fv/Fm. The most sensitive bands with high correlation coefficients were concentrated in the range of 650–850 nm, and the absolute values of the highest correlation coefficients were 0.84, 0.73, and 0.72, respectively. (2) The CC-CARS algorithm could effectively screen the characteristic wavelengths sensitive to Fv/Fm. The number of sensitive bands extracted by FD, MSC, and SNV pre-treatment methods were 14, 13, and 16 which only accounted for 2.33%, 2.16%, and 2.66% of the total spectral wavelength (the number of full spectral bands is 601), respectively. (3) The BPNN models were established based on the above sensitive wavelengths, and it was found that MSC-CC-CARS-BPNN had the highest prediction accuracy, and its testing set R2, RMSE and RPD were 0.74, 1.88% and 2.46, respectively. The results can provide technical references for hyperspectral data pre-processing and rapid and non-destructive monitoring of chlorophyll fluorescence parameters.