Berries are popular by consumers for improving vision, lowering blood sugar, improving circulation, and cardiovascular protection. They are usually small, thin-skinned, and fragile, with inconsistent ripening times. Harvesting robots are able to accurately determine the ripeness of fruits, avoiding pulp breakage and nutrient loss caused by manual squeezing. This work reviews the development and application of berry harvesting robots with market prospects in recent years. Next, this paper discusses the key technologies of berry picking robots, including fruit detection and localization technology, motion planning technology, and end-effector and harvesting mechanism. It also discusses the challenges currently faced in the development of berry harvesting robots, including external factors such as unstructured working environments and internal technical difficulties such as robot design and control. To address these challenges, future berry picking robots should focus on developing weak supervision recognition models based on deep learning, high-speed collision-free multi-arm collaborative harvesting technology, and high fault-tolerant harvesting technology to improve picking efficiency and quality, reduce fruit damage, and promote the automation and intelligence of the berry harvesting.

Swarm control technology, leveraging artificial intelligence algorithms to coordinate multiple devices, offers an effective solution for developing precision and intelligent operation systems in orchard management. This paper focuses on the core technologies underpinning swarm coordination and reviews the current state of research on collaborative communication, path planning, task allocation, and formation control, with reference to both domestic and international studies. Based on the full growth cycle of fruit trees, encompassing monitoring, precision management, and harvesting, the paper summarizes the applications and research progress of swarm control technology at each stage. Furthermore, it identifies key challenges in applying swarm technology to orchard environments, including low efficiency in heterogeneous system coordination, delayed responses to dynamic conditions, resource constraints in large-scale swarm systems, and limited adaptability to agricultural contexts, and offers strategic recommendations to address these limitations.

In the conventional agricultural production process, the harvesting of mature fruits is frequently dependent on the observation and labor of workers, a process that is often time-consuming and labor-intensive. This study proposes an enhanced YOLOv7 detection and recognition model that incorporates a cross-spatial-channel 3D attention mechanism, a prediction head, and a weighted bidirectional feature pyramid neck optimization. The motivation for this study is to address the issues of uneven target distribution, mutual occlusion of fruits, and uneven light distribution that are prevalent in harvesting operations within orchards. The experimental findings demonstrate that the proposed model achieves an mAP@0.5–0.95 of 89.3%, representing an enhancement of 8.9% in comparison to the initial network. This method has resolved the issue of detecting and positioning the harvesting manipulator in complex orchard scenarios, thereby providing technical support for unmanned agricultural operations.