Detection of Rice Seedling Rows Based on Hough Transform of Feature Point Neighborhood
Abstract
The detection of rice seedling rows is essential for precision agriculture and automatic navigation. A method based on Hough transform of feature point neighborhood was proposed to detect rice seedling rows, which can effectively solve the effects of weed distribution with different densities, different light intensities, curvature changes of seedling rows and other factors. The method had three main steps: the establishment of images database of rice seedling rows, feature point extraction of rice seedlings and the recognition of seedling row centerlines. Firstly, the image database of rice seedling rows under different light conditions (sunny and cloudy days), different weed density distributions and seedling growth status was established during the weed germination period; and then the object detection model based on Faster RCNN network was adopted to detect the positions of rice seedlings; finally, the proposed Hough transform algorithm based on the feature point neighborhood was used to recognize the center line of the seedling row. Experiments indicated that the proposed method had an average accuracy of 92% on the test set, and an average recognition accuracy of seedling rows less than 05° under high and low weed density distributions. It was not sensitive to isolated weed noise and light changes, and can also accurately recognize seedling rows with large curvatures. Therefore, the proposed method had good robustness and recognition accuracy.
Keywords: detection of rice seedling rows, image database, Faster RCNN network, feature point neighborhood, Hough transform
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