# The Application Status and Trends of Machine Vision in Tea Production
## 5 Application Challenges and Trends  
Conducting research on the application of machine vision technology to tea production and gradually achieving intelligent control and refined management of the entire process of the tea industry is of great significance for ensuring the high yield, quality, efficiency, safety, and health of the tea industry, and achieving sustainable development of the industry. With the unremitting efforts of scholars and research institutions, breakthroughs have been made in research on intelligent tea production based on machine vision. A series of superior algorithms have been developed and used, and future research should mainly include the following focus areas:  
1. Combination of multiple types of sensors. At present, most of the tea industry applications based on machine vision technology are based on visible light range images. However, a single-vision algorithm often cannot meet the information collection needs in the complex natural environment of tea planting, and a machine vision system with multiple types of sensors participating in information integration will fill this gap. The application of hyperspectral and multispectral cameras has been widely tested and applied in the areas of tea diseases, pests, and grading; technologies such as drones, space remote sensing, and 3D point clouds based on depth cameras have expanded applications such as growth information monitoring and yield estimation; in the future, with the development of sensor technology, machine vision systems combining multiple types of sensors will become a research and application avenue with great potential for development. 2. Establishment of multilevel standard datasets. In the current production and application of research on tea, due to different tea varieties and researchers, the generality of datasets is not strong, algorithms and datasets are often for a specific region or variety, and there is a lack of multitype comparison. Referring to examples in other areas of machine vision, in the future, we should collect tea images of different varieties, from different production seasons, at different price grades, from different production areas, and under different light, so as to expand the dataset of tea image samples and enrich the diversity of tea samples. A database of multivariety and multigrade tea bud leaves should be established to improve the universality of the algorithm. 3. Research on identification and location strategies. Because of the natural growth of tea buds, they are located at different heights and planes, which often leads to occlusion at different shooting angles, and severe occlusion can easily occur in the edge areas of the same image. Meanwhile, tea buds are small targets that grow on multiple branches, with complex positional information. Due to the vibration in the natural environment caused by natural wind and mechanical picking, there are great challenges in the identification and coordinate positioning in picking. Research on addressing these recognition and target localization challenges will become a major hot topic in future research. 4. Research on joint application of multiple systems. In addition to network construction and model training, the installation and application in production environments are also important aspects. In addition to being limited by the accuracy and efficiency of image analysis algorithms and network transmission rates, performance differences caused by different hardware devices can also have an impact on the entire system. In addition, from the return of software information to the specific functional implementation of hardware, the joint operation of multiple systems is still full of challenges. Most researchers often focus on solving a certain part of the problem and improving performance, leading to the inability to effectively solve practical application problems.