Abstract:
Here, a quick and accurate extraction was proposed for the navigation baselines of corn rhizomes using gradient descent and corner detection. The images of corn rhizomes were also captured at the jointing leaf stage in the field. First, the images were segmented by the 2G-B-R and the maximum between-class variance. The morphological processing was also implemented to improve the image quality. The vertical projection was then obtained to accumulate the denoised image pixels by column. Second, a feature point search method was also proposed using gradient descent. As such, the time-consuming and false feature points were avoided in the traditional peak point method. The initial point position was determined after smoothing the curve. Subsequently, the negative gradient direction of the position was used as the search direction. This direction was the fastest descending direction of the current position, where the minimum value position was found the fastest. The gradient of the mean square error loss function was also used as the descending step size. Iteratively, the location of the minimum value was achieved during this time. Therefore, the improved method enhanced the searching efficiency for the fewer errors of feature point search than the traditional. Third, the correct positioning point of the corn rhizome was retained to remove the most pseudo feature points using the corner detection. Specifically, each feature point was traversed to compare the gradient change of the pixel value in each direction of the feature point. The reason was that there were too many pseudo-feature points in the traditional searching, leading to incorrectly eliminating the positioning points of corn rhizomes. Finally, random sampling was used to fit the straight line, in order to reduce the interference of the false feature points from the rest. Since the center of the window was a corner point, the grayscale change of the point was the largest before and after moving. The larger weight coefficient demonstrated that the point contributed much more to the grayscale change, as the window moved. Once the grayscale changes of points farther from the center of the window were gradually approaching smooth, the weight coefficients of these points were set to be smaller, indicating that the point contributed much less to the grayscale changes. Consequently, several windows were moved in the surrounding direction, when the window function detected that there was no grayscale change around. Once the window was close to the edge feature line, the window was moved along the edge direction. The window reached the corner to realize the detection, as the surrounding grayscale changed greatly. Anyway, a random sampling was consistent to fit the navigation baseline. Different data sets were repeatedly selected to verify the model, thus iterating until a better model was estimated. The angle bisector of two navigation reference lines served as the cornfield navigation line. The test was performed on an industrial computer with the Intel Pentium(R) CPU G3250 @ 3.20GHZ, 4GB memory, and Windows 7 (64-bit) operating system under the integrated development environment of Python3.7 (Anaconda). Experimental results show that the improved model can be expected to better serve complex environments, indicating strong real-time performance, and the robust even in the absence of seedlings and weeds, compared with the traditional. The average processing accuracy rate was up to 92.2%. An average of 215.7 ms was used to process an image with a resolution of 1280 pixels×720 pixels. This finding can provide a reliable and real-time navigation path for the intelligent agricultural machinery in the corn field.