Extraction and Research of Crop Feature Points Based on Computer Vision

• Main Authors: Jingwen Cui, Jianping Zhang, Guiling Sun, Bowen Zheng
• Format: Article
• Language: English
• Published: MDPI AG 2019-06-01
• Series: Sensors
• Subjects: computer vision; Kinect v2 sensor; YOLOv3; feature point; visual positioning; point cloud image
• Online Access: https://www.mdpi.com/1424-8220/19/11/2553

Based on computer vision technology, this paper proposes a method for identifying and locating crops in order to successfully capture crops in the process of automatic crop picking. This method innovatively combines the YOLOv3 algorithm under the DarkNet framework with the point cloud image coordinate matching method, and can achieve the goal of this paper very well. Firstly, RGB (RGB is the color representing the three channels of red, green and blue) images and depth images are obtained by using the Kinect v2 depth camera. Secondly, the YOLOv3 algorithm is used to identify the various types of target crops in the RGB images, and the feature points of the target crops are determined. Finally, the 3D coordinates of the feature points are displayed on the point cloud images. Compared with other methods, this method of crop identification has high accuracy and small positioning error, which lays a good foundation for the subsequent harvesting of crops using mechanical arms. In summary, the method used in this paper can be considered effective.