This study investigated the effects of different concentrations of catalpa seed oil (CSO) and pomegranate seed oil (PSO) on body weight and intestinal flora in mice. The results showed that the addition of 10% w/t CSO or PSO had a more significant effect than control (soybean oil) on reducing the body mass index of mice, which was reduced by 24.94% ± 0.08% (p < 0.01) and 25.88% ± 0.11% (p < 0.01), respectively. Histological observations indicated that no significant changes in the development and structure of mouse intestinal tissue were detected as a result of CSO and PSO. Using bioinformatics technology, CSO and PSO were used to study the clustering of operational taxonomic units and the relative abundance of microorganisms in intestinal bacteria. The analysis found that both functional lipids could reduce the relative abundance of γ-Proteobacteria. It is suggested that CSO and PSO have a regulatory effect on the structure of intestinal microflora in mice. The development reported in this paper provided theoretical support for the functional research and in-depth development of CSO and PSO.

As an important index of a plant’s N nutrition, leaf nitrogen content (LNC) can be quickly monitored in real time with hyperspectral information, which is helpful to guide the precise application of N in cotton leaves. In this study, taking cotton dripping in Xinjiang, China, as the object of study, five N application treatments (0, 120, 240, 360, 480 kg·ha-1) were set up, and the hyperspectral data and the N content of main stem functional leaves at the cotton flower and boll stage were collected. The results showed that (1) comparing the correlations of the three types of spectral data from the original spectra, first derivative spectra, and second derivative spectra with the LNC of cotton, the first derivative spectra increased the correlation between the reflectance in the peak and valley ranges of the spectral curves and the LNC of cotton; (2) in the three hyperspectral regions of VIS, NIR, and SWIR, all R2 values of the estimation model for the LNC of cotton established based on the characteristic wavelengths of the original and the first derivative spectra were greater than 0.8, and the model accuracy was better than that of the second derivative spectra; and (3) the normalized root mean square error (n-RMSE) values of the validated model using MLR, PCR, and PLSR regression methods were all in the range of 10–20%, indicating that the established model could well estimate the nitrogen content of cotton leaves. The results of this study demonstrate the potential of the three hyperspectral domains of VIR, NIR, and SWIR to estimate the LNC of cotton and provide a new basis for hyperspectral data application in crop nutrient monitoring.

In the present study, hyperspectral imaging and remote sensing of fluorescence were integrated to monitor the nitrogen content in leaves of drip-irrigated cotton at different growth periods in northern Xinjiang, China. Based on the spectrum and chlorophyll fluorescence parameters of nitrogen content in cotton leaves of different growth periods obtained through the shuffled frog-leaping algorithm (SFLA), the successive projection algorithm (SPA), grey relational analysis (GRA), and competitive adaptive reweighted sampling (CARS), a monitoring model of nitrogen content in cotton leaves was established via on hyperspectral imaging, chlorophyll fluorescence parameters, and spectral-fluorescence data fusion. The results showed that: (1) there were significant positive correlations between the chlorophyll fluorescence parameters Fv'/Fm', Fv/Fm, Yield, Fm, NPQ, and the nitrogen content at each growth period. (2) The effectiveness of chlorophyll fluorescence parameters in inversion of nitrogen content was the highest at the budding period and the blooming period, and the coefficients of determination (R2) of the validation sets were 0.745 and 0.709, respectively. (3) In the monitoring model for cotton leaf nitrogen in the blooming period that was established based on the decision-level algorithm and spectral-fluorescence data fusion, the R2 value of the training set reached 0.961, and that of the validation set was 0.828. In conclusion, the findings of this study suggest that the feature-level fusion and decision-level fusion algorithms of spectral-fluorescence data can effectively improve the accuracy and reliability of cotton leaf nitrogen monitoring.

The early and accurate monitoring of crop yield is important for field management, storage needs, and cash flow budgeting. Traditional cotton yield measurement methods are time-consuming, labor-intensive, and subjective. Chlorophyll fluorescence signals originate from within the plant and have the advantages of being fast and non-destructive, and the relevant parameters can reflect the intrinsic physiological characteristics of the plant. Therefore, in this study, the top four functional leaves of cotton plants at the beginning of the flocculation stage were used to investigate the pattern of the response of chlorophyll fluorescence parameters (e.g., F0, Fm, Fv/F0, and Fv/Fm) to nitrogen, and the cumulative fluorescence parameters were constructed by combining them with the leaf area index to clarify the correlation between chlorophyll fluorescence parameters and cotton yield. Support vector machine regression (SVM), an artificial neural network (BP), and an XGBoost regression tree were used to establish a cotton yield prediction model. Chlorophyll fluorescence parameters showed the same performance as photosynthetic parameters, which decreased as leaf position decreased. It showed a trend of increasing and then decreasing with increasing N application level, reaching the maximum value at 240 kg·hm-2 of N application. The correlation between fluorescence parameters and yield in the first, second, and third leaves was significantly higher than that in the fourth leaf, and the correlation between fluorescence accumulation and yield in each leaf was significantly higher than that of the fluorescence parameters, with the best performance of Fv/Fm accumulation found in the second leaf. The correlation between Fv/Fm accumulation and yield in the top three leaves combined was significantly higher than that in the top four leaves. The correlation coefficient between Fv/Fm accumulation and yield was the highest, indicating the feasibility of applying chlorophyll fluorescence to estimate yield. Based on the machine learning algorithm used to construct a cotton yield prediction model, the estimation models of Fv/F0 accumulation and yield of the top two leaves combined as well as top three leaves combined were superior. The estimation model coefficient of determination of the top two leaves combined in the BP algorithm was the highest. In general, the Fv/F0 accumulation of the top two leaves combined could more reliably predict cotton yield, which could provide technical support for cotton growth monitoring and precision management.