Vision-based edge detection between plant column and soil of ningxia lycium barbarum garden

The cultivation of Lycium barbarum (L.barbarum) is a highly traditional and advantageous industry in Ningxia, China, and has strong development prospects. At present, the protection, fertilization, picking, and other production aspects of L.barbarum are generally inefficient and labor intensive, presenting a bottleneck that restricts the development of the industry. Developing intelligent production equipment in the form of a 'general self-moving host platform + operation module' is an urgent task for the healthy development of the L.barbarum industry. A self-planning, self-organizing, host platform must be able to perform adaptive navigation in complex unstructured environments. For this purpose, a method of edge detection that can distinguish between the plant column and soil is required. Using a color difference model with Otsu's method for image segmentation, a corrected gradient image based on the marking method is employed to remove small noise regions and then perform edge detection. Experiments demonstrate that one particular color model offers strong adaptability for light and shadow, which is good for distinguishing between the plant column and soil. The proposed method can effectively detect the edges between the plant column and soil, laying the foundation for detecting a suitable path for a self-moving platform and visual navigation.