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 G, 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.

Film covering is one of the most important parts during cotton planting. Multiple advantages remain, such as the heat preservation and moisture retention, inhibition of weed growth, fertilizer utilization rate, as well as the reduction of pests and diseases for the high yield. Among them, the film of cotton seedlings has been limited to low film-breaking efficiency. However, the labor-intensive manual breaking cannot fully meet the needs of large-scale cotton planting. Intelligence and precision operations are often required for the cotton film breaking using artificial intelligence in recent years. Intelligent machines for the cotton film breaking can be expected to accurately and rapidly identify the target of cotton seedlings under the film. In this study, a lightweight detection model, YOLOv11n-PRML, was proposed to optimize using YOLOv11n as follows: (1) A PConv-CGLU hybrid module was proposed to combine the PConv (Partial Convolution) of FasterNet and CGLU (Convolutional Gated Linear Unit) of TransNext. The C3k2 module was refactored to reduce the model complexity for the feature extraction; (2) An RSCD (Rep Shared Convolutional Detection) head with a heavy parameter-sharing strategy was employed to improve the accuracy and processing speed of the model in small target detection tasks; (3) The loss function was optimized as MPDIoU (Minimum Points Distance Intersection over Union) to improve the detection performance in dense environments; (4) The model was lightweighted using the LAMP (Layer-Adaptive Magnitude-Based Pruning) strategy. The TIDE (Toolkit for Identifying Detection and Segmentation Errors) evaluation indicator was introduced to evaluate the performance. The superiority of the YOLOv11n-PRML model was verified to detect the cotton seedling under film. Ablation tests were carried out to compare the different models. The experimental results show that the YOLOv11n - PRML model attains a precision of 90.1% and a mean Average Precision 0.5 (mAP_0.5) of 89.6%, representing increases of 1.8 and 1.0 percentage points, respectively, compared to the original YOLOv11n model. Its detection speed is improved to 114.4 frames per second. The localization error, missed ground truth error, and model size are 0.83, 0.92, and 4.0 MB, respectively, showing decreases of 0.32, 0.85, and 1.5 MB compared to the original model. Compared to the models like YOLOv5s-S (YOLOv5s-ShuffleNetV2), YOLOv7-tiny-M (YOLOv7-tiny-MobileNetV3), YOLOv8n-G (YOLOv8n-GhostNetV2), YOLOv9t, YOLOv10n, and YOLOv12n, the model's mAP_0.5 increased by 0.5, 4.3, 0.1, 4.0, 4.8, and 1.6 percentage points, respectively, while the model size was reduced by 2.8, 6.8, 1.2, 0.3, 1.8, and 1.3 MB, respectively. The YOLOv11n and YOLOv11n-PRML models were deployed on the NVIDIA GeForce RTX 2070 Ti platform, respectively, and optimized by TensorRT high-performance operators and Int8 quantization technology. The test results show that the detection accuracy mAP_0.5 of the YOLOv11n model is 87.6%, and the detection speed is 60.7 frames per second, while the detection accuracy mAP_0.5 of the YOLOv11n-PRML model is 89.1%, and the detection speed is 80.3 frames/s. In terms of inference time, the inference time of the model before and after the improvement is 16.3 ms and 11.1 ms respectively. YOLOv11n-PRML outperforms YOLOv11n in all indicators on the mobile terminal. In conclusion, the proposed YOLOv11n-PRML model provides technical support for the detection of cotton seedlings under film in complex environments and facilitates its practical application in real-world scenarios. It also offers a valuable reference for the development of intelligent film-breaking machinery. Future research will focus on expanding the dataset to include seedlings from multiple cotton varieties and conducting extensive comparative experiments to enhance the model's versatility and robustness.