# Application of Machine Vision Technology in Citrus Production
## 5 Existing Problems and Prospect  
With the rapid development of computer hardware and image processing technology, the application of machine vision technology in the field of citrus production is more and more extensive, mainly focused on the picking robot location recognition, pest and disease monitoring, and fruit quality evaluation, and gradually realized industrialization and intelligence. In recent years, the research on citrus production based on machine vision technology has made great progress, but there are still some problems. Future research and application will focus on the following aspects:  
1. Achieve diversity of dataset samples At present, the citrus research dataset based on machine vision technology mainly consists of RGB color images taken by cameras or mobile phones and spectral images collected by spectral imagers, while there are few 3D image datasets containing more information. Subsequent studies can use RGB-D cameras to shoot and obtain color image information and depth image information at the same time to improve the accuracy of citrus target recognition. In addition, the number of open source datasets of citrus images is less than that of other fruit crops, and the disease types are mostly huanglongbing and canker disease, which affects the accuracy and stability of identification to a certain extent. In the future, the number of datasets and disease types should be increased to meet more research needs.
2. Developing agricultural robots suitable for complex environments Machine vision technology is a modern development of emerging technology, and the prospect and potential is endless. Compared with standardized and idealized industrial environments, irregular natural agricultural environments are more complex. When citrus is affected by unpredictable factors such as different growth states, light intensity, and leaf occlusion, the robot under working conditions may receive wrong signals due to these complex environmental changes, thus, making wrong judgments or failing to make the next choice immediately, which seriously interferes with image extraction and recognition. Therefore, how to improve on the basis of exiting agricultural robots and overcome the complexity and irregularity of the natural environment is a key point of future development.
3. Develop high-quality image processing hardware and intelligent parallel algorithms In visual information image processing, computers are needed to carry out a series of follow-up work. In order to achieve fast and accurate processing, efficient serves are needed to improve the quality of hardware to reduce the burden of the host. Deep learning network models have a large number of parameters, large memory, and take a long time during operation, which requires high quality hardware devices. At present, the most commonly used algorithm is the serial algorithm, whose processing speed cannot meet the work requirements, and some algorithms are only suitable for a particular crop, due to a lack of universality. The parallel algorithm is developed or the parallel serial algorithm is realized, so as to improve the analysis and processing efficiency of image system.
4. Add dynamic monitoring mode In the process of citrus production, whether it is quality monitoring or picking recognition and positioning, the target objects are mostly static, and there are few fruit monitoring studies under the state of movement. In reality, not all objects are absolutely static, and when affected by the external environment, citrus swings to a certain extent, and most of the existing studies have been carried out under artificial ideal environment, so the dynamic recognition effect is poor. Therefore, considering the factors encountered in the real environment, increasing the dynamic monitoring of target objects is also a major direction of future research.