Dwarf apple is one of the most important forms of garden economy, which has become a new engine for rural revitalization. The effective detection of coloring and ripening apples in complex environments is important for the sustainable development of smart agricultural operations. Addressing the issues of low detection efficiency in the greenhouse and the challenges associated with deploying complex target detection algorithms on low-cost equipment, we propose an enhanced lightweight model rooted in YOLOv7. Firstly, we enhance the model training performance by incorporating the Squeeze-and-Excite attention mechanism, which can enhance feature extraction capability. Then, an SCYLLA-IoU (SIoU) loss function is introduced to improve the ability of extracting occluded objects in complex environments. Finally, the model was simplified by introducing depthwise separable convolution and adding a ghost module after up-sampling layers. The improved YOLOv7 model has the highest AP value, which is 10.00%, 5.61%, and 6.00% higher compared to YOLOv5, YOLOv7, and YOLOX, respectively. The improved YOLOv7 model has an MAP value of 95.65%, which provides higher apple detection accuracy compared to other detection models and is suitable for potted dwarf anvil apple identification and detection.

Zucchini growth in greenhouse environments presents significant challenges for fruit recognition and picking point localization due to characteristics such as foliage occlusion, high density, structural complexity, and diverse fruit morphologies. Current recognition and localization algorithms exhibit limitations including low accuracy, restricted applicability, and procedural complexity, falling short of the requirements for precise and robust intelligent harvesting. To address these issues, this study constructs a zucchini dataset of 942 images using an Intel RealSense D455 depth camera and a smartphone, and proposes a novel keypoint detection model named YOLOv8n-Pose-DSW. The model introduces three key enhancements compared with YOLOv8n-Pose. First, the conventional upsample operator is replaced with an adaptive point sampling operator called Dysample, improving detection accuracy while reducing GPU memory consumption. Second, a Slim-Neck structure is designed to decrease computational overhead through lightweight bottleneck architecture, while preserving robust feature representation. Third, the WIoU-v3 loss is adopted to optimize bounding box regression for object detection, thereby enhancing localization accuracy. Experimental results demonstrate that YOLOv8n-Pose-DSW achieves a zucchini detection P, R, mAP@50, and mAP@50–95 of 92.1%, 90.7%, 94.0%, and 71.4%, respectively. These metrics represent improvements of 3.3%, 11.7%, 7.4%, and 15.4%, respectively, over the original model. For picking point localization, the improved model attains a P of 93.1%, R of 89.5%, mAP@50 of 95.6%, and mAP@50–95 of 95.2%, corresponding to gains of 8.8%, 11.0%, 11.3%, and 27.9% over the original model. Further error analysis shows that picking point localization errors are concentrated within the 0–4-pixel range, demonstrating enhanced localization precision critical for practical harvesting applications. The proposed algorithm effectively addresses greenhouse environmental challenges and provides essential technical support for intelligent zucchini harvesting systems.

In practical beef cattle monitoring scenarios, automated body condition scoring (BCS) based on single-frame visual information can be affected by posture variation, occlusion, and image quality changes. This study proposed an episode-level BCS assessment framework using synchronized RGB-D videos, integrating multi-frame visual information, attention-based multiple instance learning (Attention-MIL), and a dorsal geometry-based quality control mechanism to improve reliability under complex acquisition conditions. The publicly available Raramuri Criollo dataset was reorganized into a dataset containing 3815 RGB-D frame pairs, 52 animal identity groups, and 151 episodes, and evaluated using animal-wise three-fold cross-validation. Predictions were generated only when an episode contained at least three quality-filtered frames and reliable angle estimation. With an average coverage of 0.7345 after quality gating, the final model achieved 61.66% accuracy, a Macro-F1 score of 0.5680, and a quadratic weighted kappa (QWK) of 0.6339 for five-class BCS classification. Additionally, 89.71% of predictions were within one BCS category of the reference scores. Three-class management classification achieved a Macro-F1 score of 0.8059. Ablation experiments showed complementary contributions from RGB and depth information, and the combined angle and quality-statistic features improved several metrics in the original ablation. Additional matched experiments did not establish an independent angle benefit but supported multi-frame aggregation and within-Episode prediction consistency. The proposed RGB-D multi-frame fusion approach demonstrates potential for automated beef cattle body condition monitoring under controlled acquisition conditions.

Existing selective sweet pepper harvesting systems use industrial arms and high-performance computers, which limits adoption on small farms and replication by researchers. We developed a low-cost, open-source modular harvesting robot based on Raspberry Pi 5 and a 3D-printed manipulator. The system has three modules. First, an improved YOLOv13n detector combines GhostConv, ReLU6, and DIoU Loss. It reaches 96.9% mAP@0.5 for fruits and 95.1% for peduncles with 2.35 M parameters. Compared with the YOLOv13n baseline, parameters and GFLOPs drop by 4.1% and 3.2%, while overall mAP@0.5 falls by 1.8 percentage points. Second, an adaptive size measurement and multi-criteria decision strategy uses the correlation between median depth and tilt angle (R² = 0.824), giving a mean absolute error of 5.0 mm and a 95.4% effective hit rate. Third, a ROS 2-based full-stack platform enables closed-loop perception–decision–execution. Laboratory tests give a selected-target harvesting success rate of 95.4% (103/108) and an overall success rate of 85.8% (103/120), with 1.9% fruit damage. Motion planning and execution consume 83% of cycle time. All hardware designs, ROS 2 packages, and model weights are released.

The accurate detection and non-contact weight estimation of zucchini fruits are crucial for automated harvesting systems. This study presents a novel weakly supervised oriented object detection method for zucchini fruit recognition and weight prediction in complex greenhouse environments. Our approach, termed H2RBox-v2-SF, introduces three key enhancements to the original H2RBox-v2 model. First, the Swin Transformer V2 (SwinV2) is adopted as the backbone network to replace 50-layer Residual Networks (ResNet-50), significantly strengthening feature extraction capabilities. Second, the Bi-directional Feature Pyramid Network (BiFPN) is employed instead of the original Feature Pyramid Network (FPN) to achieve more efficient multi-scale feature fusion. Third, the FPDIoU loss is introduced to replace the CircumIoU loss, enhancing the accuracy and efficiency of bounding box regression. Furthermore, we propose a Morphology-based Fruit Weight Estimation (MFWE) algorithm that leverages depth information for non-contact weight estimation. Experimental results demonstrate that the improved model achieves an AP@0.75 of 87.8%, a precision of 69.8%, and a recall of 91.5%, representing improvements of 9.6%, 5.0%, and 4.7% respectively over the original model. Additionally, the weight estimation achieves a mean absolute error (MAE) of 55.05 g, a coefficient of determination ($R^2$) of 0.899, and a root mean square error (RMSE) of 63.59 g. The proposed method achieves high accuracy for ‘Jinghu No. 43’ zucchini fruit detection and weight estimation under greenhouse conditions, offering an effective technical solution for automated zucchini harvesting.