The accuracy of apple fruit recognition in orchard environments is significantly affected by factors such as occlusion and lighting variations, leading to issues such as missed and false detections. To address these challenges, particularly related to occluded apples, this study proposes an improved apple-detection model, MSOAR-YOLOv10, based on YOLOv10. Firstly, a multi-scale feature fusion network is enhanced by adding a 160 × 160 feature scale layer to the backbone network, which increases the model’s sensitivity to small local features, particularly for occluded fruits. Secondly, the Squeeze-and-Excitation (SE) attention mechanism is integrated into the C2fCIB convolution module of the backbone network to improve the network’s focus on the regions of interest in the input images. Additionally, a Diverse Branch Block (DBB) module is introduced to enhance the performance of the convolutional neural network. Furthermore, a Normalized Wasserstein Distance (NWD) loss function is proposed to effectively reduce missed detections of densely packed and overlapping targets. Experimental results in orchards indicate that the proposed improved YOLOv10 model achieves precision, recall, and mean average precision rates of 89.3%, 89.8%, and 92.8%, respectively, representing increases of 3.1%, 2.2%, and 3.0% compared to the original YOLOv10 model. These results validate that the proposed network significantly enhances apple recognition accuracy in complex orchard environments, particularly improving the operational precision of harvesting robots in real-world conditions.

This paper presents a dynamic task planning approach for multi-arm apple-picking robots based on a deep reinforcement learning (DRL) framework incorporating Long Short-Term Memory (LSTM) networks and Proximal Policy Optimization (PPO). In the context of rising labor costs and labor shortages in agriculture, automated apple harvesting is becoming increasingly important. The proposed algorithm addresses key challenges such as efficient task coordination, optimal picking sequences, and real-time decision-making in complex, dynamic orchard environments. The system’s performance is validated through simulations in both static and dynamic environments, with the algorithm demonstrating significant improvements in task completion time and robot efficiency compared to existing strategies. The results show that the LSTM-PPO approach outperforms other methods, offering enhanced adaptability, fault tolerance, and task execution efficiency, particularly under changing and unpredictable conditions. This research lays the foundation for the development of more efficient, adaptable robotic systems in agricultural applications.

In structured orchard environments, harvesting robots operate where rigid bodies (e.g., trunks, poles, and wires) coexist with flexible foliage. Strict avoidance of all obstacles significantly compromises operational efficiency. To address this, this study proposes an end-to-end autonomous harvesting framework characterized by an “avoid-rigid, push-through-soft” strategy. This framework explicitly propagates uncertainties from sensor data and reconstruction processes into the planning and policy phases. First, a multi-task perception network acquires 2D semantic masks of fruits and branches. Class probabilities and instance IDs are back-projected onto a 3D Gaussian Splatting (3DGS) representation to construct a decision-oriented, semantically enhanced 3D scene model. The policy network accepts multi-channel 3DGS rendered observations and proprioceptive states as inputs, outputting a continuous preference vector over eight predefined motion primitives. This approach unifies path planning and action decision-making within a single closed loop. Additionally, a dynamic action shielding module was designed to perform look-ahead collision risk assessments on candidate discrete actions. By employing an action mask to block actions potentially colliding with rigid obstacles, high-risk behaviors are effectively suppressed during both training and execution, thereby enhancing the robustness and reliability of robotic manipulation. The proposed method was validated in both simulation and real-world scenarios. In complex orchard scenarios, the proposed AE-TD3 algorithm achieves a harvesting success rate of 77.1%, outperforming existing RRT (53.3%), DQN (60.9%), and TD3 (63.8%) methods. Furthermore, the method demonstrates superior safety and real-time performance, with a collision rate reduced to 16.2% and an average operation time of only 12.4 s. Results indicate that the framework effectively supports efficient harvesting operations while ensuring safety.