Grayscale distribution of maize canopy based on HLS-SVM method

It is a crucial step locating the maize plant or even its target location precisely during the intelligent agricultural equipment working in farmland. Therefore, the segmentation of plants from the background image is one of the important research contents of agricultural machine vision. Under the background of significant color difference, the current method can effectively complete maize canopy segmentation and plant location identification. However, under the background of no-significant color difference, there is no robust and high-precision method for maize canopy segmentation and plant location identification. In this study, it was found that the grayscale of maize canopy had gradient distribution trend along the radial direction. The Hue Saturation Value color space and Support Vector Machine method was used to segment 600 maize canopy images, then the polynomial regression method was used to find out the functional relationship between grayscale gradient and canopy diameter. The functional relationship gave identification results of canopy central region under different gray gradient distribution. The result provided a theoretical basis for accurate identification and rapid location of maize plant center at seedling stage, and provided accurate position coordinate and yaw information for field navigation of agricultural intelligent equipment such as plant protection UAV.