# The Application Status and Trends of Machine Vision in Tea Production
## 3 Intelligent Tea Picking
### Target Recognition and Localization
[45] established an improved mask R-CNN model to identify the main shooting parts by calculating the areas of multiple connected domains of the mask. Then, they calculated the minimum bounding rectangle of the main part, determined the tea axis, and finally obtained the position coordinates of the sampling point. [PERSON] et al. [46] eliminated background interference by identifying binary maps of a single bud, a single leaf, and two leaves of tea, and then found corner points using the [PERSON] algorithm, selecting the lowest point as the picking point. But currently, these two improved mask R-CNN models can only determine the two-dimensional pixel coordinates of the image. [PERSON] [47] installed a CMOS image sensor in conjunction with a tea cutter at the end of the SCARA robotic arm and proposed a \"one eye multi position\" 3D stereo vision system. After multiple displacements and shots, the matching similarity can reach 90%, providing theoretical basis and technical support for intelligent tea picking. [PERSON] et al. [48] used the first-order radial distortion algorithm based on the multi-pose calibration method for binocular cameras [49] to obtain three-dimensional coordinates, and then used the hand-eye calibration method to identify the change relationship. The experimental results show that the proposed algorithm has high recognition accuracy, with an accuracy rate of 97.5%, and lower positioning error, with a maximum error of only 1.33 cm. It can be further evaluated for autonomous harvesting operations. Compared with the above methods, the deep learning model based on convolutional neural networks has good recognition performance. Combined with deep cameras, it can meet the requirements of tea bud recognition and localization under natural light after enriching the dataset, providing strong technical support for the development of smart agriculture. Point cloud \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Camera Type**} & **RGB Binocular** & \multirow{2}{*}{**Structured Light**} & \multirow{2}{*}{**TOF**} \\  & **Camera** & & \\ \hline Mainstream Brands & Dajiang, Oni & Kinect V1, Realsense & Kinect V2, V3 \\ \hline \multirow{4}{*}{Working principle} & Passive: triangulation calculation based on mathing results for RGB image features & Active: actively projecting known encoded light sources to improve matching feature performance & Active: direct measurement based on the flight time of infrared lasers \\ \hline \multirow{4}{*}{Measuring range} & 0.1 m\(\sim\)20 m (the farther the distance, the lower the accuracy) & 0.1 m\(\sim\)20 m (the farther the distance, the lower the accuracy) & 0.1 m\(\sim\)20 m (the farther the distance, the lower the accuracy) \\ \hline \multirow{4}{*}{Measurement accuracy} & \multirow{2}{*}{0.01 mm\(\sim\)1 mm} & \multirow{2}{*}{0.01 mm\(\sim\)1 mm} & \multirow{2}{*}{Up to centimeter level} \\  & & & \\ \hline \multirow{4}{*}{Environment limitations} & Due to significant changes in light intensity and object texture, it cannot be used at night & The indoor effect is good, but it will be affected to some outdoor light & Not affected by changes in lighting and object texture, but will be affected by reflective objects \\ \hline Resolution ratio & Up to 2K resolution & Up to 1280 \(\times\) 720 & Usually 512 \(\times\) 424 \\ \hline \multirow{2}{*}{Frame rate} & 1 to 90 FPS & 1 to 30 FPS & Up to hundreds of FPS \\ \hline Software complexity & Higher & Medium & Higher \\ \hline \multirow{2}{*}{Consumption} & \multirow{2}{*}{Lower} & \multirow{2}{*}{Medium} & High power consumption \\ \cline{1-1}  & & & \\ \end{tabular}
\end{table} 
Table 2: Comparison of three different methods. technology has a good effect on plant pose detection. To automatically pluck tea shoots in fields, [PERSON] et al. [50] developed an algorithm framework that includes detection and localization. The YOLOv3 model was used to detect tea shoots, whereas the plucking position was obtained by combining the growth characteristics and a sleeve scheme.