The accurate identification and location of obstacles in agriculturalenvironment is one of the most important technologies for intelligent agricultural machinery. Aiming at the problem that pedestrians are difficult to detect and locate accurately in the complex orchard environment, a method of pedestrian obstacle detection and location based on binocular camera and improved YOLOv3 target detection algorithm was proposed. In this method, the left and right views were collected by zed binocular camera, and the distance information of image pixels was calculated based on parallax principle. One side of the RGB image was used as the input of the improved YOLOv3 algorithm which by introduced the tree feature fusion module, and the position information of pedestrian obstacles in the image was obtained. And then the three-dimensional coordinates relative to the camera were calculated based on the pixel position information obtained by the binocular camera. Experiment carried on the open pedestrian detection dataset in orchard environment of the National Robotics Engineering Center of Carnegie Mellon University which contained different motion states (motion and static), different pose states (normal and unnormal) and different object scales (large, medium and small). Results showed that the average precision and recall rate of the improved YOLOv3 pedestrian detection model in agriculture reached 95.34% and 91.52%, respectively, which were higher than that of the original model (94.86% and 90.19%), and the detection speed was 30.26f/ms. Meanwhile, the positioning accuracy of pedestrian obstacles was 1.65% in Z direction, and 3.80% in maximum. This method can locate pedestrian accurately and fast, providing reliable information for the obstacle avoidance of the unmanned agriculture machinery.