Uniform spraying by conventional plant protection drones often results in low herbicide utilization efficiency and environmental contamination, both of which are critical issues in agricultural production. To address these challenges, this study proposed a precision weed management system for maize fields that combines an improved YOLOv11n-OSAW detection model with DJI drones for variable-rate herbicide application. The YOLOv11n-OSAW model was enhanced with Omni-dimensional Dynamic Convolution (OD-Conv), the SEAM attention mechanism, a lightweight ADown module, and the Wise-IoU (WIoU) loss function, aiming to improve the detection accuracy of small and occluded weeds in maize fields. When the model was deployed on an uncrewed aerial vehicle (UAV) operating at 5 m altitude, it achieved mean Average Precision mAP@0.5 values of 97.8% and 97.0% for gramineous and broad-leaved weeds, respectively—representing increases of 2.9 and 1.6 percentage points over the baseline YOLOv11n model. Weed distribution maps generated from the detection results were used to develop site-specific herbicide prescription maps, guiding the drone to implement targeted spraying. Water-sensitive paper analysis verified that the system ensured effective droplet deposition and uniform coverage across different application rate areas. This integrated workflow, covering UAV image acquisition, weed detection, variable-rate application, and effect assessment, reduced herbicide consumption by 20.25% compared with conventional uniform spraying (450 L/ha) while maintaining excellent weed control efficiency and reducing environmental risks. The findings demonstrate that the proposed system provides a practical and sustainable solution for weed management in maize fields.

Nitrogen is one of the essential nutrient elements that affect rice growth, yield, and quality formation. Accurate and timely estimation of rice nitrogen status is fundamental for precision fertilization in agricultural fields. Hyperspectral remote sensing technology provides a promising approach for rapid and accurate acquisition of nitrogen status of rice in the field. However, traditional single-angle hyperspectral observations are easily disturbed by factors such as canopy structure, light direction, and background reflection, limiting their inversion accuracy and stability. This study is based on multi-angle polarimetric hyperspectral data obtained from an unmanned aerial vehicle platform. It extracts features from multi-angle polarimetric spectra based on three algorithms: successive projections algorithm (SPA), competitive adaptive reweighted sampling, and relevant features. The input weight and hidden layer bias of the extreme learning machine (ELM) model were optimized by the whale optimization algorithm (WOA) and caterpillar fungus optimization algorithm (CFO), taking the sensitive band of optimal viewing angle as input. Finally, an inversion model of rice canopy nitrogen content (CNC) based on multi-angle polarization hyperspectral data was established. The results demonstrate that the inversion results of the combination of SPA-(30°) + SPA-(45°) observation angles and feature selection methods are optimal, and multi-angle fusion significantly improves the model’s ability to characterize CNC, with higher stability and accuracy than single-angle modeling. The R² of CFO-ELM on the training set and test set reach 0.8553 and 0.8274, respectively, which is significantly better than the original ELM and WOA-ELM, becoming the optimal CNC inversion model in this study. The rice CNC inversion model based on multi-angle polarimetric hyperspectral data constructed in this study provides a specific reference for the rapid detection of rice CNC.

Well-facilitated farmland plays an important role in stabilizing and increasing crop yields, advancing agricultural mechanization, and improving production efficiency. Field-road quality directly affects machinery access and the transport of agricultural inputs and harvested crops. Current acceptance inspections rely mainly on field surveys and spot measurements, resulting in limited spatial coverage, low efficiency, and poor reproducibility. Therefore, a method is needed to rapidly survey entire field-road networks, extract road extents and structural indicators, and generate verifiable inspection records. Centimeter-resolution UAV imagery enables flexible and repeatable data acquisition, but deriving acceptance-oriented indicators remains challenging. Narrow field roads are readily obscured by crops and shelterbelt shadows or confused with cropland textures, resulting in blurred boundaries, discontinuities, and false detections. We developed a lightweight UAV-based framework integrating field-road segmentation and structural-indicator quantification. MobileNetV2 replaced the DeepLabv3+ backbone, while a Normalization-based Attention Module and Content-Aware ReAssembly of FEatures enhanced interference suppression and spatial reconstruction. Morphological processing, skeleton extraction, Euclidean distance transformation, and skeleton-graph analysis were then used to quantify road width and network connectivity. Across three training runs with different random seeds, the model achieved mean mIoU, mPA, and precision values of 93.34%, 96.75%, and 98.90%, respectively. The model had 6.14 million parameters and an inference speed of 17.04 FPS. After averaging five measurements from each road segment, the R² values between the predicted and manually measured widths were 0.650, 0.486, and 0.662 for asphalt, concrete, and gravel roads, respectively. The corresponding width MAEs were 0.130, 0.140, and 0.100 m. Connectivity analysis yielded an index of 1.00 in the first validation area, while gap repair increased the index from 0.4682 to 0.4795 in the second area. The framework supports efficient, quantitative, and traceable acceptance inspection of field-road infrastructure in well-facilitated farmland.

Wild arrowhead (Sagittaria trifolia L.) often occurs as isolated plants or compact clusters in paddy fields, yet these states are difficult to distinguish in unmanned aerial vehicle (UAV) imagery because they share similar color, texture, and leaf morphology. This study presents a two-stage framework in which an unchanged YOLOv8n detector localizes candidate targets and a dedicated Patch-cls network refines the single- or multiple-plant label. The classifier combines multi-level features, local multi-scale enhancement, and channel attention; a GhostConv variant is also evaluated to examine the efficiency trade-off. Annotation-box and detector-generated-box results are reported separately, followed by a complete-system evaluation that retains missed targets, false positives, duplicate detections, localization errors, and classification errors. Across three random seeds, the proposed Patch-cls obtained 94.00 ± 0.34% accuracy, 83.81 ± 1.19% Macro-F1, and 73.18 ± 3.91% multiple-class Recall. In the complete test pipeline, Macro-F1 increased from 0.5459 to 0.5539 and multiple-class F1 from 0.4224 to 0.4384, while mean average precision at an intersection over union (IoU) of 0.50 (mAP50) decreased slightly from 0.7023 to 0.7016. The optimized pipeline achieved 88.89 frames per second (FPS) on an NVIDIA RTX A4000 with approximately 1.62 GB peak allocated graphics processing unit (GPU) memory. The results indicate that Patch-cls can improve category balance under detector-generated crops, although the overall gain is modest and does not replace the need for stronger localization and dense-target separation.