## 4 Challenges and Future Directions of Target Recognition Research for Picking Robots  
### Existing Challenges  
1. An unstructured orchard environment raises the difficulty of fruit recognition. In the actual working environment, the change in lighting conditions easily affects the color characteristics of the target fruit, and the shading of branches, leaves, and other background objects, as well as overlapped fruit, easily lead to missed fruit and other problems in the target recognition system. The traditional target recognition technology is substantially limited to the complex environment of actual work, and the fruit recognition efficiency is low. Although deep learning technology can improve the target recognition performance of picking robots in complex environments, there are still many uncontrollable influencing factors, and the stability of the visual recognition  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Vision-Based 3D Reconstruction Technology of Fruit** & **Application to Crops** & \multicolumn{2}{c}{**Technical Characteristics and Performance Indicators**} & \multicolumn{1}{c}{**Limitations**} & \multicolumn{1}{c}{**References**} \\ \hline  & & Proximity recognition & & & \\  & & positioning is high, can be used in the natural environment, & Affected by light conditions and when the fruit is shaded, and complicated to calculate when stereo & [9, 88, 89] \\  & & & recognition accuracy of 88\%. & matching. & \\ \hline  & & Overcomes the influence of changing light conditions, with a wide measurement range and a combined recognition accuracy of 85\%. & Sparse point cloud generation for fine branches, low accuracy of object edge positioning. & [90, 91, 92] \\ \hline \end{tabular}
\end{table}
Table 4: Comparing the research results of vision-based 3D reconstruction technology of fruit.  
system needs to be considered. In order to obtain more accurate 3D information on fruits, vision-based 3D reconstruction technology can be used as an effective approach, but the existing 3D reconstruction technology still faces various challenges. For example, the 3D reconstruction technology based on a stereo vision system has difficulty accurately matching the overlapped fruit in three dimensions, and the 3D reconstruction technology based on an RGB-D vision sensor has the problem of insufficient filling rate when collecting slender branches. Therefore, an unstructured orchard environment raises the difficulty of fruit recognition.
2. The real-time nature of the vision system in picking robots makes it difficult to meet the actual production needs. To improve the recognition performance of its vision system, the recognition algorithms are often examined. Although the method can effectively improve the recognition accuracy of the algorithm, this also makes the structure of the algorithm even more complex and the operation time longer. Consequently, actual production needs are often not met.
3. The scale of the data set affects the robustness of the target recognition system of the picking robot. To improve the robustness of the target detection model, the images in the dataset need to contain different lighting conditions, shading degrees, fruit overlapping, and growth cycles of their own characteristics. Therefore, the data images need to be acquired at different times, and at the same time, large-scale acquisition tasks need to be completed within a specific period. Therefore, large-scale data set acquisition is one of the important tasks of fruit target recognition.
4. The generality of target recognition models needs to be improved. In most of the existing studies, the improvement of target detection algorithms for fruits only targets a specific situation, while in a working environment, many different situations may occur simultaneously, such as fruits at different growth stages. Therefore, the development of highly generalized target detection models is beneficial to improve the accuracy of the picking robot's decision-making.