## 4 Challenges and Future Directions of Target Recognition Research for Picking Robots
### The Future Directions of Development  
1. Research on accurate fruit recognition technology for complex environments. The complex working environment is still one of the constraints for the development of picking robots. Although many researchers have proposed various improvement measures for fruit detection models, different lighting conditions, overlapping and occlusion will still affect the recognition performance of picking robots. At the same time, the complex environment also poses certain challenges to the 3D reconstruction of fruits, and the changes in lighting conditions and fruit overlapping pose challenges to the 3D construction of the visual recognition system. Therefore, it is important to improve the robustness, generalization, and versatility of the visual recognition system for picking robots in complex environments.
2. Research on target recognition technology for fruit in a dynamic environment. During the operation of the picking robot, the fruit is generally not completely stationary, and the wind and picking action will cause the fruit to oscillate dynamically. Although the design of the end-effector of the picking robot can accommodate certain position deviations, the randomness and complexity of fruit oscillation may still lead to the failure of the end-effector picking, thus damaging the picked fruit or the end-effector.
3. Lightweight model research for fruit target recognition. Since picking robots pose high requirements for target detection models, they not only require a fast detection speed to achieve real-time detection but also small model sizes so that the model can be embedded in the devices. Therefore, subsequently, the focus should be on developing lightweight target detection models that can be used in edge devices for real-time fruit detection and improving the performance of visual recognition systems in embedded devices.  
4. Target recognition technology research for small-scale data sets. Target recognition technology often requires a large number of data sets covering various aspects. The process of data acquisition and labeling thus takes a longer time and greater effort. However, the research using small-scale data models in existing target recognition techniques is underrepresented. Therefore, how to use small-scale data sets for model training is an important research direction.  
The presented work was under the supervision of [PERSON] and [PERSON]: conceptualization, methodology, software, and writing--original draft; [PERSON]: validation, writing--review and editing; [PERSON] and T.C.: methodology and writing--review; [PERSON] and L.T.: validation. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Guangdong Province Modern Agricultural Industry Technology System Innovation Team Construction Project (Tea)--Tea Industry Innovation Team Facility and Mechanization Post Expert (grant No. 2023 KJ120), The \"14 th Five-Year Plan\" Guangdong Province Agricultural Science and Technology Innovation in the Ten main Directions \"Unveiling the List of Hanging\" Project--Lingnan Characteristic Fruit Intelligent Harvesting Technology (grant No. 2022 SDZG03) and Guangdong Province Special fund for scientific and technological innovation strategy (\"Climbing Plan\" Special fund) (grant No. pdijh202 za0072).  
Not applicable.  
This study did not report any data.  
The authors acknowledge the editors and reviewers for their constructive comments and all the support for this work.  
The authors declare no conflict of interest.