Relative chlorophyll content (SPAD) is an important index for characterizing the nitrogen nutrient status of plants. Continuous, rapid, nondestructive, and accurate estimation of SPAD values in wheat after heading stage can positively impact subsequent nitrogen fertilization management strategies, which regulate grain filling and yield quality formation. In this study, the estimation of SPAD of leaf relative chlorophyll content in spring wheat was conducted at the experimental base in Wuyuan County, Inner Mongolia in 2021. Multispectral images of different nitrogen application levels at 7, 14, 21, and 28 days after the wheat heading stage were acquired by DJI P4M UAV. A total of 26 multispectral vegetation indices were constructed, and the measured SPAD values of wheat on the ground were obtained simultaneously using a handheld chlorophyll meter. Four machine learning algorithms, including deep neural networks (DNN), partial least squares (PLS), random forest (RF), and Adaptive Boosting (Ada) were used to construct SPAD value estimation models at different time from heading growth stages. The model’s progress was evaluated by the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAPE). The results showed that the optimal SPAD value estimation models for different periods of independent reproductive growth stages of wheat were different, with PLS as the optimal estimation model at 7 and 14 days after heading, RF as the optimal estimation model at 21 days after heading, and Ada as the optimal estimation model at 28 d after heading. The highest accuracy was achieved using the PLS model for estimating SPAD values at 14 d after heading (training set R² = 0.767, RMSE = 3.205, MAPE = 0.060, and R² = 0.878, RMSE = 2.405, MAPE = 0.045 for the test set). The combined analysis concluded that selecting multiple vegetation indices as input variables of the model at 14 d after heading stage and using the PLS model can significantly improve the accuracy of SPAD value estimation, provides a new technical support for rapid and accurate monitoring of SPAD values in spring wheat.

The maximum quantum efficiency of photosystem II (Fv/Fm) is a widely used indicator of photosynthetic health in plants. Remote sensing of Fv/Fm using MS (multispectral) and RGB imagery has the potential to enable high-throughput screening of plant health in agricultural and ecological applications. This study aimed to estimate Fv/Fm in spring wheat at an experimental base in Hanghou County, Inner Mongolia, from 2020 to 2021. RGB and MS images were obtained at the wheat flowering stage using a Da-Jiang Phantom 4 multispectral drone. A total of 51 vegetation indices were constructed, and the measured Fv/Fm of wheat on the ground was obtained simultaneously using a Handy PEA plant efficiency analyzer. The performance of 26 machine learning algorithms for estimating Fv/Fm using RGB and multispectral imagery was compared. The findings revealed that a majority of the multispectral vegetation indices and approximately half of the RGB vegetation indices demonstrated a strong correlation with Fv/Fm, as evidenced by an absolute correlation coefficient greater than 0.75. The Gradient Boosting Regressor (GBR) was the optimal estimation model for RGB, with the important features being RGBVI and ExR. The Huber model was the optimal estimation model for MS, with the important feature being MSAVI2. The Automatic Relevance Determination (ARD) was the optimal estimation model for the combination (RGB + MS), with the important features being SIPI, ExR, and VEG. The highest accuracy was achieved using the ARD model for estimating Fv/Fm with RGB + MS vegetation indices on the test sets (Test set MAE = 0.019, MSE = 0.001, RMSE = 0.024, R² = 0.925, RMSLE = 0.014, MAPE = 0.026). The combined analysis suggests that extracting vegetation indices (SIPI, ExR, and VEG) from RGB and MS remote images by UAV as input variables of the model and using the ARD model can significantly improve the accuracy of Fv/Fm estimation at flowering stage. This approach provides new technical support for rapid and accurate monitoring of Fv/Fm in spring wheat in the Hetao Irrigation District.

This study aimed to investigate the effects of applying arbuscular mycorrhizal fungi (AMF) on maize root growth and yield formation under different soil conditions. This study was conducted under sandy soil (S) and saline–alkali soil (Y), with treatments of AMF application (AM) and no AMF application (CK). The root characteristics, yield, and quality of maize were measured. High-throughput sequencing technology was employed to assess the impact of AMF on the soil microbial community structure, and the correlation between soil microbes and soil physicochemical properties was elucidated. The results show that under both sandy and saline–alkali soil conditions, AMF application significantly enhanced maize root growth, yield, grain quality, and soil available nitrogen (AN), available phosphorus (AP), and available potassium (AK) contents compared to the CK treatment. Soil microbial Alpha diversity analysis indicated that AMF application effectively increased soil microbial diversity and richness. Principal coordinate analysis (PCoA) and microbial community structure analysis revealed significant differences in bacterial communities between AM treatment in sandy soil (SAM) and CK in sandy soil (SCK), and significant differences in both bacterial and fungal communities between AM treatment in saline–alkali soil (YAM) and CK in saline–alkali soil (YCK). Furthermore, significant correlations between microbial communities and soil physicochemical properties were found, such as AN, AP, AK, soil salinity (SS), and organic matter (OM) content. AMF application had a greater impact on bacterial communities than on fungal communities. This study demonstrated that the use of AMF as a bio-fungal fertilizer was effective in improving spring maize yields, especially in terms of yield increase and quality stability in sandy and saline soils, thereby contributing to safe and sustainable cropping practices.

Land use and land cover (LULC) are fundamental units of human activities. Therefore, it is of significance to accurately and in a timely manner obtain the LULC maps where dramatic LULC changes are undergoing. Since 2017 April, a new state-level area, Xiong’an New Area, was established in China. In order to better characterize the LULC changes in Xiong’an New Area, this study makes full use of the multi-temporal 10-m Sentinel-2 images, the cloud-computing Google Earth Engine (GEE) platform, and the powerful classification capability of random forest (RF) models to generate the continuous LULC maps from 2017 to 2020. To do so, a novel multiple RF-based classification framework is adopted by outputting the classification probability based on each monthly composite and aggregating the multiple probability maps to generate the final classification map. Based on the obtained LULC maps, this study analyzes the spatio-temporal changes of LULC types in the last four years and the different change patterns in three counties. Experimental results indicate that the derived LULC maps achieve high accuracy for each year, with the overall accuracy and Kappa values no less than 0.95. It is also found that the changed areas account for nearly 36%, and the dry farmland, impervious surface, and other land-cover types have changed dramatically and present varying change patterns in three counties, which might be caused by the latest planning of Xiong’an New Area. The obtained 10-m four-year LULC maps in this study are supposed to provide some valuable information on the monitoring and understanding of what kinds of LULC changes have taken place in Xiong’an New Area.

In a study aimed at developing a rapid and nondestructive method for testing the quality of strawberries, spectral data from four strawberry varieties at different ripening stages were collected using a geophysical spectrometer, primarily focusing on the 350–1800 nm band. The spectra were preprocessed using Savitzky–Golay (SG) filtering, and characteristic bands were extracted using Pearson correlation coefficient (PCC) analysis. Models for predicting strawberry quality were built using random forest (RF), support vector machine (SVM), partial least squares (PLS), and Gaussian regression (GPR). The results indicated that the SVM model exhibited relatively high accuracy in predicting anthocyanin, hardness, and soluble solids content in strawberries. For the test set, the SVM model achieved R² and RMSE values of 0.81, 0.87, and 0.89, and 0.04 mg/g, 0.33 kg/cm², and 0.72%, respectively. Additionally, the PLS model demonstrated relatively high accuracy in predicting the titratable acid content of strawberries, achieving R² and RMSE values of 0.85 and 0.03%, respectively, for the test set. These findings provided a solid foundation for strawberry quality modeling and a veritable guide for non-destructive assessment of strawberry quality.

Wine grape quality is influenced by the variety and growing environment, and the quality of the grapes has a significant impact on the quality of the wine. Tannins are a crucial indicator of wine grape quality, and, therefore, rapid and non-destructive methods for detecting tannin content are necessary. This study collected spectral data of Pinot Noir and Chardonnay using a geophysical spectrometer, with a focus on the 500–1800 nm spectrum. The spectra were preprocessed using Savitzky–Golay (SG), first-order differential (1D), standard normal transform (SNV), and their respective combinations. Characteristic bands were extracted through correlation analysis (PCC). Models such as partial least squares (PLS), support vector machine (SVM), random forest (RF), and one-dimensional neural network (1DCNN) were used to model tannin content. The study found that preprocessing the raw spectra improved the models’ predictive capacity. The SVM–RF model was the most effective in predicting grape tannin content, with a test set R² of 0.78, an RMSE of 0.31, and an RE of 10.71%. These results provide a theoretical basis for non-destructive testing of wine grape tannin content.