Wheat is a key staple crop globally, essential for food security and sustainable agricultural development. The results of this study highlight how innovative monitoring techniques, such as UAV-based multispectral imaging, can significantly improve agricultural practices by providing precise, real-time data on crop growth. This study utilized unmanned aerial vehicle (UAV)-based remote sensing technology at the wheat experimental field of the Hebei Academy of Agriculture and Forestry Sciences to capture the dynamic growth characteristics of wheat using multispectral data, aiming to explore efficient and precise monitoring and management strategies for wheat. A UAV equipped with multispectral sensors was employed to collect high-resolution imagery at five critical growth stages of wheat: tillering, jointing, booting, flowering, and ripening. The data covered four key spectral bands: green (560 nm), red (650 nm), red-edge (730 nm), and near-infrared (840 nm). Combined with ground-truth measurements, such as chlorophyll content and plant height, 21 vegetation indices were analyzed for their nonlinear relationships with wheat growth parameters. Statistical analyses, including Pearson’s correlation and stepwise regression, were used to identify the most effective indices for monitoring wheat growth. The Normalized Difference Red-Edge Index (NDRE) and the Triangular Vegetation Index (TVI) were selected based on their superior performance in predicting wheat growth parameters, as demonstrated by their high correlation coefficients and predictive accuracy. A random forest model was developed to comprehensively evaluate the application potential of multispectral data in wheat growth monitoring. The results demonstrated that the NDRE and TVI indices were the most effective indices for monitoring wheat growth. The random forest model exhibited superior predictive accuracy, with a mean squared error (MSE) significantly lower than that of traditional regression models, particularly during the flowering and ripening stages, where the prediction error for plant height was less than 1.01 cm. Furthermore, dynamic analyses of UAV imagery effectively identified abnormal field areas, such as regions experiencing water stress or disease, providing a scientific basis for precision agricultural interventions. This study highlights the potential of UAV-based remote sensing technology in monitoring wheat growth, addressing the research gap in systematic full-cycle analysis of wheat. It also offers a novel technological pathway for optimizing agricultural resource management and improving crop yields. These findings are expected to advance intelligent agricultural production and accelerate the implementation of precision agriculture.

The effective integration of aerial remote sensing data and ground multi-source data has always been one of the difficulties of quantitative remote sensing. A new monitoring mode is designed, which installs the hyperspectral imager on the UAV and places a buoy spectrometer on the river. Water samples are collected simultaneously to obtain in situ assay data of total phosphorus, total nitrogen, COD, turbidity, and chlorophyll during data collection. The cross-correlogram spectral matching (CCSM) algorithm is used to match the data of the buoy spectrometer with the UAV spectral data to significantly reduce the UAV data noise. An absorption characteristics recognition algorithm (ACR) is designed to realize a new method for comparing UAV data with laboratory data. This method takes into account the spectral characteristics and the correlation characteristics of test data synchronously. It is concluded that the most accurate water quality parameters can be calculated by using the regression method under five scales after the regression tests of the multiple linear regression method (MLR), support vector machine method (SVM), and neural network (NN) method. This new working mode of integrating spectral imager data with point spectrometer data will become a trend in water quality monitoring.

This study investigates the dynamic changes in wheat canopy spectral characteristics across seven critical growth stages (Tillering, Pre-Jointing, Jointing, Post-Jointing, Booting, Flowering, and Ripening) using UAV-based multispectral remote sensing. By analyzing four key spectral bands—green (G), red (R), red-edge (RE), and near-infrared (NIR)—and their combinations, we identify spectral features that reflect changes in canopy activity, health, and structure. Results show that the green band is highly sensitive to chlorophyll activity and low canopy coverage during the Tillering stage, while the NIR band captures structural complexity and canopy density during the Jointing and Booting stages. The combination of G and NIR bands reveals increased canopy density and spectral concentration during the Booting stage, while the RE band effectively detects plant senescence and reduced spectral uniformity during the ripening stage. Time-series analysis of spectral data across growth stages improves the accuracy of growth stage identification, with dynamic spectral changes offering insights into growth inflection points. Spatially, the study demonstrates the potential for identifying field-level anomalies, such as water stress or disease, providing actionable data for targeted interventions. This comprehensive spatio-temporal monitoring framework improves crop management and offers a cost-effective, precise solution for disease prediction, yield forecasting, and resource optimization. The study paves the way for integrating UAV remote sensing into precision agriculture practices, with future research focusing on hyperspectral data integration to enhance monitoring models.

The effective integration of aerial remote sensing data and ground multi-source data has always been one of the difficulties of quantitative remote sensing. A new monitoring mode is designed which installs the hyperspectral imager on the UAV and places a buoy spectrometer on the river. Water samples are collected simultaneously to obtain in situ assay data of total phosphorus, total nitrogen, COD, turbidity and chlorophyll during data collection. The cross correlogram spectral matching (CCSM) algorithm is used to match the data of the buoy spectrometer with the UAV spectral data to reduce the UAV data noise significantly. An absorption characteristics recognition algorithm (ACR) is designed to realize a new method for comparing UAV data with laboratory data. This method takes into account the spectral characteristics and the correlation characteristics of test data synchronously. It is concluded that the most accurate water quality parameters can be calculated by using the regression method under five scales after the regression tests of multiple linear regression method (MLR), support vector machine method (SVM) and neural network (NN) method. This new working mode of integrating spectral imager data with point spectrometer data will become a trend in water quality monitoring.

This study investigates the high-quality development of rural e-commerce in China, focusing on its role in bridging the urban-rural divide and supporting rural revitalization in the context of digital village initiatives. Using data from 31 provinces (2018–2022), we assess the development levels, spatial linkages, and key drivers of rural e-commerce. Employing a combination of game-theoretic weighting, a modified gravity model, social network analysis, and fuzzy-set qualitative comparative analysis (fsQCA), the study identifies significant regional disparities in e-commerce development. Key drivers include infrastructure, technology, and policy, with central provinces such as Henan and Shandong playing pivotal roles, while peripheral regions face challenges due to lower digital integration. The study highlights two primary pathways for development: one driven by infrastructure and productivity improvements, and the other by technological innovation. These findings offer policy recommendations for enhancing regional collaboration and reducing the urban-rural divide through targeted investments in infrastructure and digital integration.