# The Application Status and Trends of Machine Vision in Tea Production
## 2 Pest and Disease Detection
### Pest Detection  
At present, the detection and classification of crop pests in China is still in its early stages, and there is a lack of dedicated disease and pest datasets for tea to use in research. Currently, pest monitoring of field crops still relies on manual experience for judgment. Farmers and management personnel can often classify captured pests based on their experience, but for rare pests, it is often difficult for farmers to identify them. There are six main pests in tea trees, namely, tea sesame beetle, tea geometrid, tea geometrid larvae, false-eyed small green leafhopper, copper-green golden turtle, and tea horned bug. Among them, tea geometrid and grey tea geometrid are the main pests of Chinese tea trees, seriously endangering the yield and quality of tea and causing huge economic losses to tea production [25]. With the continuous development of machine vision-based algorithms such as support vector machines and neural networks, the monitoring technology of pests has also made great progress. However, its application in the tea industry is still in its early stages. In 2009, [PERSON] et al. [26] extracted 17 morphological features from binary images of stored grain pests and normalized them in their research on image recognition. Then, they used the ant colony algorithm to automatically extract seven optimal feature subspaces from them. Finally, they chose a support vector machine classifier to classify nine types of grain pests, with a recognition rate of over 95%. [PERSON] et al. [27] proposed a method for identifying rice stem borer pests based on a convolutional neural network model. After image preprocessing, images containing rice stem borer adults, larvae, eggs, or pupae were selected, and a 10-layer convolutional neural network model was designed with an accuracy of 89.14%. [PERSON] [28] used the improved YOLOv3 to detect 12 types of tea pests, and made relevant improvements to the model in terms of lightweight and sample imbalance. Compared with the preimprovement version, the accuracy can be improved by 3 to 6 percentage points. [PERSON] et al. [29] used a small green leafhopper insect detection and reporting system and a precise target insect pest sound and light prevention and control system for a comparative experiment, as shown in Figure 3. The results showed that based on the color plate trapping and counting mode, the recognition accuracy can reach over 89.29%. In the tea garden used for the experiment on precise sound and light prevention and control, the rate of decline in the insect population of the small green leafhopper reached 76%. The model can achieve real-time prediction of tea garden pest dynamics, achieve efficient and precise control of target pests in tea gardens, and achieve zero application of chemical pesticides throughout the year in experimental tea gardens. The tea disease and pest detection system is easily affected by many factors such as light intensity, background, camera resolution, etc. Therefore, combining multiple types of cameras and various intelligent sensor devices to achieve real-time, efficient, and comprehensive detection of plant diseases and pests is one of the future research directions that can promote agricultural modernization and intelligence. To segment pest and disease images in complex backgrounds, it is often necessary to combine several segmentation \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Year** & **Disease Type** & **Task** & **Color Model** & **Method** & **Advantage** & **Disadvantage** \\ \hline \multirow{10}{*}{[PERSON] et al. [24]} & \multirow{10}{*}{2021} & \multirow{10}{*}{Tea algal spot} & \multicolumn{4}{c}{Establishment} & \multirow{10}{*}{None} & \multirow{10}{*}{The model effect of principal component analysis (PCA) combined with linear discriminant analysis (LDA) high as 98.9\% environment} \\  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._methods to achieve ideal segmentation results.