Nighttime pineapple detection for automated harvesting robots is a significant challenge in intelligent agriculture. As a crucial component of robotic vision systems, accurate fruit detection is essential for round-the-clock operations. The study compared advanced end-to-end style transfer models, including U-GAT-IT, SCTNet, and CycleGAN, finding that CycleGAN produced relatively good-quality images but had issues such as the inadequate restoration of nighttime details, color distortion, and artifacts. Therefore, this study further proposed an enhanced CycleGAN approach to address limited nighttime datasets and poor visibility, combining style transfer with small-sample object detection. The improved model features a novel generator structure with ResNeXtBlocks, an optimized upsampling module, and a hyperparameter optimization strategy. This approach achieves a 29.7% reduction in FID score compared to the original CycleGAN. When applied to YOLOv7-based detection, this method significantly outperforms existing approaches, improving precision, recall, average precision, and F1 score by 13.34%, 45.11%, 56.52%, and 30.52%, respectively. These results demonstrate the effectiveness of our enhanced CycleGAN in expanding limited nighttime datasets and supporting efficient automated harvesting in low-light conditions, contributing to the development of more versatile agricultural robots capable of continuous operation.

Precise localization of the fruit stem picking point is crucial for robots to achieve efficient harvesting operations. However, in unstructured orchard environments, citrus fruit stems are easily obscured by branches and leaves and affected by factors such as overlapping fruits. This leads to poor picking localization accuracy for robots, impacting their autonomous picking efficiency. Therefore, this paper proposes a method for estimating the posture of citrus fruit stems and performing picking operations under environmental occlusion, based on the YOLO-OBB algorithm. First, the YOLOv5s algorithm detects the ROI of citrus, combined with depth information to obtain their 3D point clouds. Second, the OBB algorithm constructs oriented point cloud bounding boxes to determine stem orientation and picking point locations. Finally, through hand–eye pose transformation of the robotic arm, the end-effector is controlled to achieve precise picking operations. Experimental results indicate that the average picking success rate of the YOLO-OBB algorithm reaches 82%, representing a 50% improvement over approaches without fruit stem estimation. This conclusively shows that the proposed algorithm provides precise fruit stem pose estimation, effectively enhancing robotic picking success rates under constrained fruit stem detection conditions. It offers crucial technical support for autonomous robotic harvesting operations.

Pineapple harvesting in natural orchard environments faces challenges such as high occlusion rates caused by foliage and the need for complex spatial planning to guide robotic arm movement in cluttered terrains. This study proposes an innovative visual detection model, Yolov10n-SSE, which integrates split convolution (SPConv), squeeze-and-excitation (SE) attention, and efficient multi-scale attention (EMA) modules. These improvements enhance detection accuracy while reducing computational complexity. The proposed model achieves notable performance gains in precision (93.8%), recall (84.9%), and mAP (91.8%). Additionally, a dimensionality-reduction strategy transforms 3D path planning into a more efficient 2D image-space task using point clouds from a depth camera. Combining the artificial potential field (APF) method with an improved RRT* algorithm mitigates randomness, ensures obstacle avoidance, and reduces computation time. Experimental validation demonstrates the superior stability of this approach and its generation of collision-free paths, while robotic arm simulation in ROS confirms real-world feasibility. This integrated approach to detection and path planning provides a scalable technical solution for automated pineapple harvesting, addressing key bottlenecks in agricultural robotics and fostering advancements in fruit-picking automation.