# The Application Status and Trends of Machine Vision in Tea Production
## 2 Pest and Disease Detection
### Disease Detection
However, due to the large variation in light intensity in the natural environment, the segmentation effect based on fixed thresholds cannot meet the actual needs of the field. With the development of neural network models, deep learning models trained on diverse datasets have gradually become the mainstream in research and achieved better results. With the development of modern agriculture, spectral imaging technology has gradually matured and been applied to the identification of crop diseases. Spectral imaging technology combines spectral analysis technology with image analysis technology to conduct qualitative and quantitative analysis of the tested object. In recent years, the application of research on hyperspectral and multispectral methods in tea diseases has gradually increased. [PERSON] et al. [22] proposed a tea spot recognition method based on the fusion of hyperspectral imaging technology and image processing technology. By extracting the relative spectral reflectance of sensitive bands in the region of interest as spectral features, the second principal component image after the second principal component analysis is determined as the feature image. The color and texture features of the feature image are extracted based on the color moments and grayscale co-occurrence matrix. Then, the BP neural network, optimized using the genetic algorithm, was used to improve the recognition rate to 94%, which can also provide a reference for low-altitude disease detection of plant protection drones. [PERSON] et al. [23] studied tea red leaf disease at different disease stages, collected fluorescence spectrum information through fluorescence transmission technology, and established prediction models for feature spectrum joint grayscale co-occurrence matrix texture and LBP operator texture using an extreme learning machine (ELM), which improved the recognition rate of diseases at different disease stages. [PERSON] et al. [24] applied chlorophyll fluorescence spectral feature analysis to tea disease detection. Through comparison of results, the model established by principal component analysis (PCA) combined with linear discriminant analysis (LDA) showed the best performance, with a recognition rate of 98.9%. Integrating hyperspectral and multispectral technologies into machine vision for disease identification can reduce the impact of light and enable the use of drones and other platforms to reduce damage to tea. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Ref. ** & **Year** & **Disease Type** & **Task** & **Color Model** & **Method** & **Advantage** & **Disadvantage** \\ \hline [PERSON] et al. [20] & 2021 & Tea blight & Detect, identify, and estimate the severity of the disease & None & The original image is enhanced using Retinex algorithm, and then VGG16 network is used & Only one disease of tea \\ [PERSON] et al. [20] & 2021 & Tea blight & Detect, identify, and estimate the severity the disease & The original image is enhanced using Retinex algorithm, and then VGG16