# The Application Status and Trends of Machine Vision in Tea Production
## 2 Pest and Disease Detection
### Disease Detection  
In the process of the development of tea diseases, the images of diseased leaves are more obvious and easier to obtain than those of insect pests and have a deeper impact on the quality of tea. In 2017, [PERSON] [14] extracted 34 feature components of tea diseases through adaptive filtering, color space transformation, Otsu image segmentation and other methods, and then used BP neural network, support vector machine (SVM) and random forest methods for training and selected the comprehensive optimal model. In 2019, [PERSON] et al. [15] used the Canny edge detection algorithm to extract the contour of tea disease spots. They combined the Otsu method and construction index method with contour information to extract the features of three tea tree disease images: tea round brown spot, tea red leaf spot, and tea white spot. Ultimately, they achieved the best efficiency and recognition effect, but did not consider the color changes in different disease stages. [PERSON] et al. [16] proposes an algorithm that combines simple linear iterative clustering (SLIC) with SVM. By extracting multidimensional features of pixel blocks segmented by SLIC, SVM is used for classification, and the image is then repaired and segmented, so that it can effectively extract tea disease images under complex backgrounds. [PERSON] et al. [17] used multiple preprocessing combinations, using k-means and minimum N \(\times\) The N method extracts lesions and performs super-resolution reconstruction, with the highest accuracy reaching 93.3% in the AlexNet network model. However, there is a problem of uneven distribution in the number of training images. [PERSON] et al. [18] proposed to apply low shooting learning to tea disease recognition in order to solve the problem of the difficulty in the acquisition of disease images and of the small sample size of the dataset. After C-DCGAN data augmentation, support vector machines were used for training, exhibiting an accuracy  
Figure 2: The principle of disease and pest detection using machine vision. of up to 90%. The small-sample experimental effect was significantly improved. In 2020, [PERSON] et al. [19] proposed a clustering recognition method for tea disease area images based on non-dominant sorting genetic algorithm (NSGA-II), which effectively improved the detection effect and provided an online detection system. In 2021, research carried out by [PERSON] et al. [20] aimed to detect and grade the severity of leaf blight on natural backgrounds. Firstly, the Retinex algorithm was used to enhance the original image, and a deep learning framework of \"region based fast convolutional neural network\" was used for detection. Then, the detected image was imported into the VGG16 network for training. The experimental results showed that compared with classical machine learning methods, the average detection accuracy and severity classification accuracy of this method have been improved by more than 6% and 9%, respectively. Similarly, in order to solve the problem that the traditional convolutional neural network cannot accurately and quickly recognize tea diseases due to a small and uneven distribution of tea disease samples, in 2022, [PERSON] et al. [21] proposed a tea disease recognition method based on transfer learning, used the PlantVillage dataset to obtain a pretraining model, and trained it in the improved DenseNet model. Under the situation of small samples and uneven distribution of samples, the recognition accuracy of tea diseases reached 92.66%. This model has high recognition accuracy and strong robustness and can provide a reference for intelligent diagnosis of tea diseases. Based on the above methods, color space segmentation models will select some components for further analysis, such as the G component of RGB, the H or S component of HSV(hue, saturation, value), or the a and b components of the lab model.