Cu and O co-doped porous g-C ₃ N ₄ with outstanding photocatalytic degradation of levofloxacin performance was synthesized by a one-pot thermal polymerization approach.

To address the operational demands of irregular farmland with fixed obstacles, this study proposes a full-coverage path planning framework that integrates UAV-based 3D perception and angle-adaptive optimization. First, digital orthophoto maps (DOMs) and digital elevation models (DEMs) were reconstructed from low-altitude aerial imagery. The feasible working region was constructed by shrinking field boundaries inward and dilating obstacle boundaries outward. This ensured sufficient safety margins for machinery operation. Next, segmentation angles were scanned from 0° to 180° to minimize the number and irregularity of sub-regions; then a two-level simulation search was performed over 0° to 360° to optimize the working direction for each sub-region. For each sub-region, the optimal working direction was selected based on four criteria: the number of turns, travel distance, coverage redundancy, and planning time. Between sub-regions, a closed-loop interconnection path was generated using eight-directional A* search combined with polyline simplification, arc fitting, Chaikin subdivision, and B-spline smoothing. Simulation results showed that a 78° segmentation yielded four regular sub-regions, achieving 99.97% coverage while reducing the number of turns, travel distance, and planning time by up to 70.42%, 23.17%, and 85.6%. This framework accounts for field heterogeneity and turning radius constraints, effectively mitigating path redundancy in conventional fixed-angle methods. This framework enables general deployment in agricultural field operations and facilitates extensions toward collaborative and energy-optimized task planning.

Comprehensive growth index (CGI) more accurately reflects crop growth conditions than single indicators, which is crucial for precision irrigation, fertilization, and yield prediction. However, many current studies overlook the relationships between different growth parameters and their varying contributions to yield, leading to overlapping information and lower accuracy in monitoring crop growth. Therefore, this study focuses on winter wheat and constructs a comprehensive growth monitoring index (CGIac), based on adaptive weight allocation of growth parameters’ contribution to yield, using data such as leaf area index (LAI), soil plant analysis development (SPAD) values, plant height (PH), biomass (BM), and plant water content (PWC). Using UAV data on vegetation indices, feature selection was performed using the Elastic Net. The growth inversion model was then constructed using machine learning methods, including linear regression (LR), random forest (RF), gradient boosting (GB), and support vector regression (SVR). Based on the optimal growth inversion model for winter wheat, spatial distribution of wheat growth in the study area is obtained. The findings demonstrated that CGIac outperforms CGIav (constructed using equal weighting) and CGIcv (built using the coefficient of variation) in yield correlation and prediction accuracy. Specifically, the yield correlation of CGIac improved by up to 0.76 compared to individual indices, while yield prediction accuracy increased by up to 23.14%. Among the evaluated models, the RF model achieved the best performance, with a coefficient of determination (R²) of 0.895 and a root mean square error (RMSE) of 0.0058. A comparison with wheat orthophotos from the same period confirmed that the inversion results were highly consistent with actual growth conditions in the study area. The proposed method significantly improved the accuracy and applicability of winter wheat growth monitoring, overcoming the limitations of single parameters in growth prediction. Additionally, it provided new technological support and innovative solutions for regional crop monitoring and precision farming operations.