Authors

Abstract

Aiming at the field management of plum grove in Inner Mongolia of China, taking the dense planting plum groves in Bikeqi town of Hohhot City as the research object, this paper proposed a visual navigation path detection algorithm for plum grove. By processing the video image information of plum grove, comparing RGB and HSV color space model, HSV color model was selected to separate the plant and background in V channel. Homomorphic filtering was used to highlight the region of interest in the image, Otsu was selected to segment the image, the intersection of plum trunk and ground was extracted as feature points, and the least square method was used to fit the navigation path. Through the comparative analysis of detection rate under different detection conditions in one day, the verification test of route accuracy was carried out. The experimental results show that: for dense planting plum grove, the average path detection accuracy of the algorithm is 70% and 73.3% under the condition of front light and weak light, respectively. The detection accuracy and real-time meet the requirements of plum grove field management, and the navigation baseline can be generated more accurately, which provides a preliminary basis for the realization of mechanical vision navigation in plum grove field management.

Abstract in Chinese

针对中国内蒙古地区李子园田间管理，以呼和浩特市毕克齐镇密植李子园为研究对象，该研究提出一种李子园行内做视觉导航路径检测算法。通过处理李子园图像信息，对比RGB和HSV颜色空间模型，确定选用HSV颜色模型，在V通道进行植株与背景分离。使用同态滤波将图像感兴趣区域凸显出来，选择Otsu对图像进行分割，并将李子树干与地面交点作为特征点进行提取，采用最小二乘法拟合导航路径。通过在一天内不同检测条件下检测率的对比分析，对路线精度进行验证试验。试验结果表明：对于密植李子园在顺光和弱光检测条件下，该算法的路径检测准确率平均值分别为70%和73.3%，检测准确性与实时性满足李子园田间管理要求，能够较准确生成导航基准线，为李子园田间管理机械视觉导航实现提供前期基础。