It is very notable and quite surprising to some that RGB depth (RGB-D) images contain a great deal of semantic information that transcends the capabilities offered by the aforementioned band features. Table 1 has been given to summarize and compare the use of various types of sensors for tree crowns in the above research. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensors** & \multicolumn{2}{c}{**Advantages**} & \multicolumn{2}{c}{**Disadvantages**} \\ \hline Ultrasonic sensors & \(\bullet\) & Low price and good robustness & \(\bullet\) & Large ultrasonic beam spread angle \\ • & \(\bullet\) & Easy to implement and good adaptability & \(\bullet\) & Limited resolution and accuracy of the measurements \\  & \(\bullet\) & Independent of environmental conditions & \(\bullet\) & The price is too high to be widely promoted \\ Lidar sensors & \(\bullet\) & Provide high resolution of tree canopy structure & \(\bullet\) & Complex structure \\ Infrared sensors & \(\bullet\) & Low cost and simple structure & \(\bullet\) & Influenced by light easily \\  & \(\bullet\) & Real-time and accuracy of image processing & \(\bullet\) & Highly sensitive to the weather conditions \\ Camera sensors & \(\bullet\) & Improved by machine learning significantly & \(\bullet\) & Calibration is required, and the accuracy is not as high \\  & \(\bullet\) & Depth camera can obtain precise distance & & as Lidar \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Advantages and disadvantages of the different types of sensors used for geometrical characterization of tree crown. In recent years, deep learning theory has developed rapidly with the emergence of various convolutional neural networks (CNNs) that provide new and unanticipated segmentation approaches. Compared with traditional segmentation methods, this method outputs multi-size feature maps based on candidate regions, which greatly improves the ability to extract object features. In 2021, [PERSON] et al. [21] proposed an approach for orchard trees segmentation based on a deep learning CNNs, namely the Convolutional Networks for Biomedical Image Segmentation (UNet). [PERSON] et al. [22] integrated five advanced algorithms to achieve semantic tree-crown region segmentation in complex urban scenes. Shortly afterwards, [PERSON] et al. [23] proposed a semantic pixel-wise segmentation network (SegNet) intelligent pear-tree spraying system, which achieved the highest accuracy at the time: 83.79%. Their method is quite robust and distinguishes tree species. It can even separate the crowns from skies, buildings, and brush. Nevertheless, the continuity mask generated by the same kind of tree crown makes it impossible to distinguish different trees of the same type. Therefore, more advanced algorithms are needed; to this end, instance segmentation algorithms have been applied. Because instance segmentation can detect and segment object simultaneously, it can solve the drawback of semantic segmentation in not being able to distinguish between different individuals. In recent years, researchers have combined the instance segmentation algorithms with the use of depth cameras. For example, [PERSON] et al. [24] developed a new instance segmentation model called \"tiny Mask R-CNN\", which was used to detect guava fruit and tree branches. Each detected fruit and tree branch was converted into a 3D point cloud by using the RGB camera, thus enabling the detection and 3D modeling of the fruit trees. Due to the large data obtained from the point cloud, a small number of images were collected for training. Recently, [PERSON] et al. [25] combined depth information and improved mask-type region-based (R)-CNN (Mask R-CNN) to recognize cherry tomatoes and achieved an accuracy of 93.76% for fruit recognition, which is 11.53% higher than that obtained using standard Mask R-CNN. The experimental results of these studies show that the effective fusion of RGB-D helps to improve the feature expression ability of the model.