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

A Lightweight Detection Method for Citrus Fruits Based on Improved YOLO11 in Dense Occlusion Environments

To address the challenges of low detection accuracy, high miss rates, and limited model lightweightness arising from dense fruit distribution, foliage occlusion, and small fruit size during citrus fruit localisation and recognition in complex orchard environments, this study proposes a lightweight small-object citrus fruit detection model based on an improved YOLO11 architecture, termed SOCD-YOLO (YOLO for Small Object Citrus Detection). Firstly, a DLTBlock is designed by integrating element-wise multiplication with a Triplet Attention mechanism to reconstruct the C3k2 module, thereby enhancing the nonlinear fusion of high-dimensional features. This design effectively suppresses interference from occluding foliage and complex backgrounds, and improving the robustness of fruit target recognition. Secondly, the traditional downsampling operation is replaced with the ADown module, which effectively alleviates information loss during feature propagation for small-object features, while simultaneously reducing model parameters and computing complexity, thus enhancing small-object recognition accuracy. Finally, a lightweight P2FP structure is constructed to further enhance the model’s capability in identifying small and densely distributed objects, while significantly reducing the missed detection rate. Experimental findings indicate that, on the CitDet dataset, the proposed model achievesimprovements of 4.3%, 6.3%, and 5.6% in Precision, Recall, and mAP, respectively, while reducing the parameter count and model size to 1.5 M and 3.5 MB. Moreover, the FPS reaches 103.5 frames/s. On the Tomato and PASCAL VOC 2007 datasets, overall performance is consistently improved. Compared to existing object detection models, SOCD-YOLO demonstrates enhanced performance in terms of citrus fruit detection accuracy and robustness, providing a valuable reference for artificial intelligence based real-time fruit detection and position measurement in densely occluded environments.

A Feature Enhancement Framework for Joint Mango Fruit and Stem Detection in Complex Orchard Environments

Reliable joint detection of mango fruits and stems is an essential upstream perception task for robotic harvesting, but remains challenging because stems are small, slender, frequently occluded, and visually degraded by illumination variation. This study proposes MangoNET, a YOLOv11n-based framework for joint mango fruit and stem detection in complex orchard environments. A P2 high-resolution detection head preserves fine spatial information for small targets, while SPPF-ELAN aggregates local and contextual features for partially visible objects. SENet recalibrates channel responses under illumination variation, and WIoU v3 regulates bounding-box samples with different localization qualities. A dataset containing 1782 original images of Tainong and Jinhuang mangoes was collected from two orchards and data augmentation was applied only to the training set, increasing its size from 1172 to 2886 images through rotation, contrast adjustment, and Gaussian noise addition. MangoNET achieved fruit and stem F1-scores of 0.920 and 0.916, respectively, with mAP50 and mAP50–95 values of 0.941 and 0.690. Compared with YOLOv11n, mAP50 and mAP50–95 increased by 1.6 and 2.9 percentage points, respectively, while stem recall increased from 0.877 to 0.906. Source-image-independent five-fold cross-validation yielded mean mAP50 and mAP50–95 values of 0.944 and 0.711, respectively. Pilot evaluations using images acquired by a UAV and an RGB-D camera in a geographically distinct orchard suggested that MangoNET could maintain detection performance in a different orchard environment. MangoNET supplies fruit and stem candidate regions for subsequent association, harvesting-point localization, and robotic manipulation.

ScaleEdgeFusion-Network: a lightweight and efficient model for citrus maturity detection in complex orchards

Accurate detection of citrus fruit maturity is essential for robotic selective harvesting in orchards yet remains challenging due to complex environmental conditions. This paper presents ScaleEdgeFusion-Net (SEF-Net), a lightweight object detection framework built on YOLO11n for efficient citrus maturity recognition in real-world orchard environments, achieved through three key innovations: an Adaptive Multi-scale Edge Enhancement module integrated with the backbone to improve fruit discriminability; an Enhanced Multi-scale Feature Extraction module replacing standard spatial pyramid pooling to strengthen robustness against complex environmental conditions; and an Efficient Feature Fusion and Dynamic Sampling Neck redesigned to leverage dynamic upsampling and channel attention for high detection accuracy with minimal computational overhead. Experimental results demonstrate that SEF-Net achieves superior performance with 93.0% mAP@0.5 while maintaining only 2.0 million parameters and 5.2 G, resulting in a compact model size of 4.3 MB, and delivers the highest inference speed (132 FPS) among all compared models. Compared to state-of-the-art detectors—including general-purpose models (YOLOv5n, YOLOv8n, YOLO11n, etc) and citrus-specific models such as ORD-YOLO and LightSal-DETR—the proposed method achieves higher detection accuracy with significantly lower computational requirements. These results indicate that SEF-Net provides an effective balance between accuracy and efficiency, making it suitable for deployment on resource-constrained harvesting robots in precision agriculture applications.

WCAF-YOLO: A Lightweight Detection Architecture for Multi-Variety Tomatoes in Unstructured Orchards

WCAF-YOLO is proposed, a lightweight two-dimensional tomato detector based on a modified YOLOv26n architecture that replaces the P3 backbone downsampling operation with space-to-depth convolution (SPD-Conv) to retain fine-grained spatial information.

DCG-YOLO: an Efficient Lightweight Model for Field Weed Detection

Deep Learning for In-Field Occlusion Handling and Real-Time Fruit Detection Under Dense Canopy Conditions

Robotic fruit harvesting in dense canopies remains challenging due to occlusion, variable illumination, and fruit-foliage similarity. This review synthesises recent deep learning-based detection systems, with particular focus on occlusion mitigation through multi-stage perception pipelines. The literature reveals that attention mechanisms and multi-scale feature fusion have emerged as dominant strategies for detecting partially visible fruits under leaf cover and overlapping branches. Surveyed studies report mean Average Precision scores ranging from 85–95% on orchard imagery, with YOLOv8 and Faster R-CNN serving as common benchmarks. Real-time feasibility on embedded hardware has been demonstrated across multiple systems, though the review identifies persistent gaps, including inconsistent reporting across lighting conditions and fruit maturity stages, as well as ongoing challenges in reducing false positives from visual clutter. By categorising trade-offs between accuracy, efficiency, and robustness, this review consolidates current knowledge and highlights directions toward reliable autonomous harvesting in complex agricultural environments.