Weeds severely reduce rice yield and quality, making reliable in-field detection of weed and rice seedling essential for automated weed management. Although deep learning-based object detection techniques have shown significant potential in automatically distinguishing crops from weeds, existing models often suffer from large model sizes, high computational complexity, and insufficient feature extraction. To address these issues, this study proposes a lightweight multi-angle object detection model named MAL-YOLOv5 (Multi-Angle Lightweight YOLOv5), based on the YOLOv5 (You Only Look Once version 5) framework, which effectively reduces model complexity while maintaining detection performance. Specifically, the lightweight MobileNetV3 architecture is adopted to replace the original backbone network, significantly decreasing the number of parameters without compromising accuracy. Furthermore, the neck network of MAL-YOLOv5 is enhanced by integrating spatial and channel reconstruction convolution (SCConv) and a single-shot feature aggregation module (SCCSP), which reduces spatial and channel redundancies in the convolutional module, thereby compressing the neck network and improving feature representation. Additionally, a rotated bounding box with angular information is introduced for annotating and detecting rice seedling and weed, which effectively mitigates the interference from background and non-target objects, enabling more precise identification. Experimental results show that the precision, recall, and mAP of the MAL-YOLOv5 model are 93.1%, 91.9%, and 93.4%, respectively. Compared to YOLOv5s_obb, the MAL-YOLOv5 model reduces the number of parameters by 80.1% and computational cost in GFLOPs (Giga Floating-Point Operations) by 81.5%, significantly minimizing model size with only marginal loss in accuracy, conserving computational and storage costs while lowering the hardware requirements for intelligent mechanical weeding equipment.

Mechanical transplanting is one of the key procedures in large-scale and high-efficiency rice production. However, the bending and squeezing are prone to occur in seedling stems during high-speed seedling separating. The resulting seedling damage can threaten the seedling establishment rate and yield. Although the large seedlings show strong stress resistance, their leaf sheath tissues are still subjected to significant impact loads at the separating stage. Particularly, the leaf sheath vascular bundles can serve as the primary load-bearing tissue in the stem. However, it is unclear on the damage mechanism of the leaf sheath vascular bundles during mechanical transplanting. It is often required for the reliable mesoscopic-scale biomechanical evidence to optimize the operating parameters of the separating-planting mechanism. This study aims to investigate the mechanical damage characteristics of the leaf sheath vascular bundles during large rice seedlings, in order to clarify the relationship between vascular bundle damage and machine parameters. The 35-day-old rice seedlings were used as research material. The leaf sheath vascular bundles were isolated after enzymatic maceration, in order to reduce the influence of the mechanical peeling on the tissue's structural integrity. Stem bending tests were conducted on the large rice seedlings. Typical loading was simulated during separation. Two bending modes were set along the short and the long axis of the elliptical cross-section. The bending displacements of 1.5, 3.0, 4.5, and 6.0 mm were applied for the bending-induced damage. After that, tensile tests were performed on the isolated vascular bundles. A systematic analysis was implemented to explore the effects of the bending mode and bending displacement on the mechanical parameters of the vascular bundles. The experimental results showed that there was no variation in the material properties of the vascular bundles after bending damage, while the tensile strength decreased markedly. The tensile strength was attributed to the more pronounced short-axis bending. The critical bending displacements for the vascular bundle damage were determined to be 3.0 and 4.5 mm under short- and long-axis bending, respectively. A biomechanical model and a bending finite element model of the leaf sheath vascular bundles in the large rice seedlings were constructed to perform mesoscopic-scale mechanical simulations. The simulation results showed that the stress was concentrated mainly in the region of the vascular bundle sheath, with the more severe stress concentration under short-axis bending. Meanwhile, the stress transmission pattern within the vascular bundle tissues remained consistent under different bending modes, indicating the bending-mode dependence in the intensity of stress concentration, rather than in the stress transfer pathway. The maximum operating speed was optimized to combine with the kinematics of the separating planting, according to the damage thresholds under different bending modes. The maximum speeds were obtained as 181 and 243 r/min under minor-and major-axis bending. These findings can provide a theoretical basis to optimize the operational parameters of the transplanting mechanism, with emphasis on the biomechanical modeling of plant tissue damage.