# Application of Machine Vision Technology in Citrus Production
## 4 Harvesting  
With the sustainable development of modern agriculture, automation technology is more and more important in the field of agricultural production. The traditional production method of citrus orchards has many problems that are not conducive to large-scale development, for example high cost and high labor intensity. These problems seriously constrain the profitability of the citrus industry. Therefore, an intelligent and automatic harvesting method is needed. The rapid and accurate identification and positioning of outdoor citrus, as well as the improvement of the picking robot's path planning algorithm, can improve the harvesting efficiency. Based on machine vision technology, some scholars have carried out corresponding research. Figure 6 shows the picking diagram of citrus-picking robot. Currently, citrus harvesting operations are mainly completed by manpower, and the large number of citrus results in high labor intensity and low productivity. Therefore, accurate identification of picking points for citrus crops is particularly important. [PERSON] [50] proposed a citrus picking point recognition system based on deep learning and feature analysis, as well as a fruit positioning system based on visual servo laser directional ranging, by separating citrus recognition and fruit positioning. This system solved the problems of light changes, bright spots, and shadow blocking during the harvesting process. [PERSON] et al. [51] proposed a citrus picking point recognition method based on Hough transform, which can effectively improve the recognition accuracy by collecting citrus images under natural environment, constructing citrus recognition models, and  
Figure 6: Harvesting diagram of citrus-picking robot. segmenting effective fruit regions. [PERSON] et al. [52] proposed a network model based on the improved YOLOv3-tiny, replacing the original loss function with DIOU loss function, using MobileNetv3-Small as the backbone extraction network and adding a new residual structure, and adding a simplified spatial pyramid pool structure into the feature extraction network. The average recognition accuracy of the improved network model is 96.52%. The path planning of the picking robot can save time and improve the picking efficiency. [PERSON] [53] used the depth camera to collect citrus images, input them into the improved mask R-CNN algorithm for target detection, then converted the pixel coordinates of citrus into world coordinates and input them into the improved ant colony algorithm for picking path planning. [PERSON] [54] built a simulation experiment platform based on the ROS system to reconstruct the citrus harvesting environment, and proposed the picking motion planning algorithm PGI-RRT* algorithm based on the pre-picking guiding point to realize the movement planning obstacle avoidance algorithm of citrus-picking robotic arm in unstructured environments. [PERSON] et al. [55] abstracted citrus-picking path-planning as a traveling dealer problem. The improved ant colony algorithm introduced an adaptive pheromone concentration updating mechanism that changes with time, which can realize the optimal picking path. In the natural environment, the growth position of citrus is random and complex, which is interfered with and blocked by branches, leaves, light, and other external environmental factors, so that the picking mechanical arm makes mistakes in the process of movement. In order to make the mechanical arm reach the target point accurately, it is necessary to study the path-planning of the mechanical arm. [PERSON] [56] converted the pixel coordinates of citrus fruit into world coordinates, built a three-dimensional target positioning model, adopted the D-H method to establish the kinematic model of the picking manipulator, and deduced the trajectory planning of quincal B-spline curve to carry out simulation experiments. [PERSON] et al. [57] proposed an improved motion planning algorithm for a citrus-picking manipulator based on Informed RRT*.