Locating weed growth points in maize fields fusing deep learning and morphological processing

Locating weed growth points is often required in laser weeding. However, the high accuracy is also confined to the small target size, irregular morphology, and mutual occlusion in cornfield environments. In this study, the apical meristem localization (SDPL) framework was proposed to integrate an improved deep learning detector with the skeleton and density peak analysis. Firstly, a lightweight model, named YOLOv11n-LBD, was developed to detect the small, irregular, and partially occluded weeds. Three enhancements were incorporated using YOLOv11n: a Lightweight Adaptive Extraction (LAE) module for efficient and adaptive feature encoding, a bidirectional feature pyramid network (BiFPN) for the multi-scale feature fusion with learnable weights, and a DyHeadDCNv4 detection head with dynamic attention and deformable convolution for refined spatial and scale-aware feature representation. The subtle features were then extracted under complex fields. A custom dataset was constructed to collect corn seedlings and five common weed species. A series of tests was also conducted to verify the framework. The results showed that the improved model achieved a mean average precision (mAP) of 94.8% and a recall of 93.2%. Compared with the baseline YOLOv11n, the parameter count and computational complexity were reduced by 0.4 million and 0.8 GFLOPs, respectively, with a real-time inference speed of 79.4 frames per second. There was a favorable balance between accuracy and efficiency suitable for mobile or embedded deployment in field robots. The SDPL localization was introduced to reduce the positional deviation between the bounding box geometric center and the actual biological growth point of a weed. The region of interest (ROI) was cropped using the YOLOv11n-LBD output. A high-quality binary mask was then generated for noise suppression using Gaussian filtering, followed by Otsu's adaptive thresholding for robust segmentation, effectively isolating the plant from the background. Morphological opening and closing operations were applied to suppress noise for the high connectivity. Subsequently, a skeleton optimization was implemented. The initial single-pixel skeleton was extracted using the Zhang-Suen parallel thinning algorithm, thus preserving the topological structure. The raw skeleton was smoothed and refined to reduce the artifacts, such as jaggedness and spurious branches. A continuous and natural skeletal representation was obtained after B-spline curve fitting. Candidate growth points were defined as the junction points of the refined skeleton branches. Finally, the precise growth points were identified to evaluate the local pixel density distribution within a circular neighborhood around each candidate. The final localization points were selected as the highest local density peak, indicating the most concentrated area of plant tissue near the stem base. Experimental validation on field images showed that there was a growth point localization accuracy of 81.2%. The accuracies were improved by 3.0 and 14.0 percentage points, respectively, compared with the key point detection and bounding box center positioning. The SDPL performed reliably for weeds with irregular shapes under partial occlusion, and even when overlapping with crop plants, where the geometrical method often failed. Robust detection and morphological-densitometric analysis can provide a precise, practical, and computationally efficient visual perception in laser weeding robots, contributing to targeted and energy-efficient weed control in sustainable agriculture.