# Research Progress and Prospects of Agricultural Aero-Bionic Technology in China
## 2 Biological Perception and Agricultural Aviation
### Binocular Stereo Vision Bionics  
[PERSON] [30], from the Massachusetts Institute of Technology, first proposed a visual computing theory, which uses two or more cameras with different angles to acquire image feature points and draw three-dimensional images. The geometric principle of binocular stereo vision is shown in Figure 1.  
Figure 1: Geometric principle of the binocular stereo vision system. In Figure 1, C1 and C2 are two cameras with optical axes parallel to each other, and P1 and P2 are the imaging points of three-dimensional space point P on two two-dimensional imaging planes, x1o1y1 and x2o2y2, respectively. The relationship between depth and parallax is as follows:  
\[Z=\frac{B\cdot f}{d} \tag{1}\]  
where \(Z\) is the depth, \(B\) is the baseline, \(d\) is the difference between p1 and p2 in the x-axis direction, and \(f\) is the focal length. Binocular stereo vision technology has the advantages of fast speed measurement at short-range, high accuracy, and no contact [31], but the accuracy is reduced during long-distance detection, and it is difficult to match the object image with low texture [32]. In practical application, camera parameter setting, illumination change, environmental change, equipment performance, etc., will cause differences in image-matching results and affect target detection accuracy [33]. At present, the research on binocular vision systems mainly focuses on the stereo matching algorithm. [PERSON] [34] proposed an improved adaptive weighting algorithm to solve the problems of occlusion and depth discontinuity in binocular matching, which greatly reduced the influence of the depth discontinuity area, optimized the parallax map, and improved the accuracy of generating the parallax map. [PERSON] [35] proposed an autonomous path planning algorithm of plant protection UAV combined with GNSS (Global Navigation Satellite System, Xianyang, China ) and binocular vision to solve the problem that plant protection UAV cannot accurately spray in mountain fruit tree planting. First, GNSS navigates the UAV to the operation area, and then the yaw angle of the UAV is adjusted by the binocular vision sensor to complete the precision operation of the UAV. [PERSON] [36] aimed at improving the efficiency and accuracy of obstacle avoidance during UAV flight, and proposed a method of combining binocular stereo vision and optical flow. The distance of obstacles is obtained through binocular stereo vision, and the instantaneous speed of obstacles to cameras is calculated by the SIFT (scale-invariant feature transform) optical flow method to create the obstacle avoidance strategy. The combined algorithm has significantly improved the calculation speed and obstacle avoidance effect. In recent years, binocular stereo vision technology has developed rapidly, and the research hotspots mainly focus on the stereo matching algorithm. The future development will also focus on optimizing the calculation speed of the algorithm, researching more comprehensive visual computing theories and matching strategies [37], and fusion of multiple algorithms, etc. Binocular stereo vision is widely used in agricultural aviation, especially plant protection UAVs. For example, the industrial-grade UAV, MATRICE M300 RTK (DJI Innovation Technology Co., Ltd., Shenzhen, China), introduced binocular vision and infrared perception sensors into the six faces of the fuselage at the same time, for the first time. Its sensing system is shown in Figure 2. The system has the characteristics of omni-directional six-way sensing, accurate positioning, fast obstacle avoidance, etc. In addition, Mavic2, Inspire2, and the phantom series of DJI UAVs are equipped with binocular vision systems and infrared sensors in different positions of the fuselage. The plant protection UAV CD-15 (Hanhe Aviation Technology Co., Ltd., Wuxi, China) uses a binocular vision system and a laser detection system to mix and match.