YOLO-PINE: A Lightweight Pineapple Object Detection Method Based on an Improved YOLO11n

GOPD-YOLO: A Lightweight Oriented Object Detection Network for Real-Time Scallion Posture Recognition

Precise and real-time posture recognition of scallions during harvesting and post-harvest processing is critical for automated conveying, orientation adjustment, bundling, and packaging. Nevertheless, their slender and flexible form, varied spatial orientations, target overlap, lighting fluctuations, background interference, and constrained computational resources of edge devices present significant obstacles to reliable visual perception. This research introduces GOPD-YOLO, a lightweight oriented object-detection network built on the YOLOv8-OBB framework. The network integrates partial-convolution-based lightweight feature extraction to minimize redundant computation, large separable-kernel attention to boost long-range structural representation, and a shared detail-enhanced detection head to improve boundary- and orientation-sensitive prediction. A custom dataset comprising 1500 conveyor-belt images under diverse scallion posture scenarios was developed for model training and assessment. GOPD-YOLO attained a precision of 90.6%, a recall of 94.5%, an mAP@0.5 of 93.2%, and an mAP@0.5:0.95 of 71.5%, with 2.38 million parameters, 6.6 GFLOPs, and a model size of 4.9 MB. Relative to YOLOv8n-OBB, GOPD-YOLO enhanced recall by 3.8 percentage points while decreasing parameter count and model size by 22.7% and 22.2%, respectively. Deployment tests were performed on the Jetson Orin NX Super platform across varying conveyor speeds, lighting conditions, and scallion stacking levels to evaluate the model’s practical utility. These results indicate GOPD-YOLO’s potential as a lightweight vision-based solution for scallion posture recognition in automated harvesting and post-harvest processing.

MFA-YOLO: a lightweight weed detection model for complex farmland environments

Fast-YOLO11n: A Lightweight and Efficient Apple Detection Model for Complex Orchard Environments

Accurate and real-time apple detection in complex orchard environments is essential for robotic harvesting but remains challenging because of illumination variation, foliage occlusion, and limited computational resources. This study proposes Fast-YOLO11n, a lightweight detector derived from the nano variant of You Only Look Once 11 (YOLO11n) and integrating three complementary components. A Fast-C3k2 module based on partial convolution (PConv) reduces redundant computation while preserving cross-layer feature transmission. A focal modulation (FM) mechanism enhances target-related responses and suppresses background interference under occlusion and uneven illumination. In addition, a parallel downsampling module, termed ADown, retains local geometric details and multi-scale semantic information during downsampling. Experiments were conducted on a field-collected orchard dataset comprising 2240 images and 22,673 annotated apple instances under diverse lighting, scale, and occlusion conditions. Fast-YOLO11n achieved mean average precision values of 75.76% across intersection-over-union (IoU) thresholds of 0.50–0.95 (mAP@50–95) and 91.29% at an IoU threshold of 0.50 (mAP@50), while operating at 366.19 frames per second (FPS) with 2.51 million parameters and 6.00 billion floating-point operations (FLOPs). Compared with the YOLO11n baseline, it improved mAP@50–95 and mAP@50 by 2.39 and 1.39 percentage points, respectively, while reducing the parameter count and FLOPs by 2.71% and 5.36%. Ablation experiments demonstrated the individual and combined effects of the three modules on detection performance and computational efficiency. The proposed model provides a favorable balance between detection accuracy and computational efficiency, indicating its potential for real-time orchard perception on resource-constrained platforms.

MGSD-YOLO: inland vessel target detection algorithm based on multiscale feature fusion

An improved YOLOv11n-based method for high-precision detection of ginkgo fruits in complex natural environments

Introduction Ginkgo fruits in natural field environments are characterized by low color saliency, small target size, severe occlusion by branches and leaves, and complex illumination variations. These factors significantly reduce real-time detection accuracy and limit the applicability of existing object detection methods in practical agricultural scenarios. Methods To address these challenges, this study proposes a real-time ginkgo fruit detection method based on an improved YOLOv11n framework. A multi-scenario dataset was constructed by collecting ginkgo fruit images under diverse lighting conditions, occlusion levels, and viewing angles, and data augmentation strategies were applied to improve sample diversity and model generalization. On this basis, a CFNet channel fusion module was embedded into the backbone network, a DynamicHead detection head was introduced to enhance multi-scale feature representation, and the original loss function was replaced with the Efficient IoU (EIoU) loss to improve bounding box regression accuracy. These improvements collectively form the proposed CED-YOLOv11n model, achieving a balanced optimization between detection accuracy and inference efficiency. The effectiveness of the model was validated through Grad-CAM visualization analysis, ablation studies, and comparative experiments with classical object detection models. Results Experimental results show that the proposed CED-YOLOv11n achieves a precision of 94.6%, a recall of 85.4%, and a mean average precision (mAP) of 93.8% on the constructed ginkgo fruit dataset. In addition, the model is lightweight, with a parameter size of only 4.96 MB, and achieves an inference speed of 53.2 FPS, demonstrating strong real-time performance. Compared with mainstream object detection models such as DETR, Faster R-CNN, and YOLOv5, the proposed method achieves superior overall detection performance. Discussion The results indicate that the proposed method effectively enhances the accuracy and efficiency of ginkgo fruit detection in complex natural environments. It provides technical support for the development of vision-based perception systems in intelligent ginkgo harvesting equipment and offers a reference for further optimization and integration of smart agricultural machinery systems.

A Lightweight model for accurate soybean pod detection: YOLO-MobilePod.

BACKGROUND Soybean pods are critical indicators for evaluating soybean yield and quality. In natural environments, they exhibit substantial morphological variability and are predominantly small-scale objects, often characterized by dense distributions and frequent occlusions. Existing object detection algorithms still face considerable limitations in detecting small-scale and overlapping targets. RESULTS To address these challenges, this study proposes a lightweight and efficient soybean pod detection model, termed YOLO-MobilePod, which is developed based on YOLOv12. The proposed model adopts MobileNetV4 as the backbone network to reduce computational complexity while enhancing multi-scale feature extraction capability. To further improve the detection performance for small-scale soybean pods, YOLO-MobilePod introduces high-resolution feature fusion strategy in the neck network, thereby strengthening the representation of small-object features. In addition, dynamic convolution (DynamicConv) is incorporated into the regression branch of the detection head, where adaptive convolutional kernel combinations are employed to enhance feature modeling capacity while achieving additional model lightweighting. This study integrated a public dataset with a self-collected dataset and conducted both ablation and comparative experiments. The Experimental results demonstrate that, compared with the YOLOv12n model, YOLO-MobilePod achieves improvements of 2.07% in recall and 4.10% in mAP50-95, while reducing the number of parameters, FLOPs, and model size by 45.31%, 4.762%, and 38.18%, respectively. In addition, the inference speed increased by 8.768%. CONCLUSIONS These results demonstrate that the proposed model exhibits effective recognition performance for soybean pods with diverse morphologies and achieving improved detection accuracy while preserving model lightweighting, thereby providing a feasible technical reference for efficient soybean pod detection.