Research on Minirhizotron Root Image Processing Method
|School||Northeast Forestry University|
|Course||Mechanical Design and Theory|
|Keywords||Minirhizotron Feature Extraction Image Mosaic Morphological Measurement|
Root is vital organ for plants to get nutrition from the soil, if it grow well has crucial effect on the whole plant, and root can form a composite structure with the soil to fix plants and to prevent soil erosion. At the same time, carbon sink of root in the ecosystem circulation can not be ignored. So the study on root is of great significance. But root is hidden, and is difficult to directly observe. Invention of minirhizotron gives people great convenience to do research on root. This article focused on image processing of root that achieved from minirhizotron, and improved the speed and accuracy of processing. After that, we also did some analysis and research on root morphology parameter measuring methods. In this paper, the main contents are as below:In this paper, we enhance and denoise root image collected by minirhizotron, reduce edge width of original fuzzy image, and prepare for the subsequent image processing. This study presents a fuzzy algorithm to classify the noise. Noise is divided into the Gauss noise, pulse noise that at the image edge and impulse noise in flat image regions, use the fuzzy weighted average filter, bidirectional multilevel median filter and the one-way multilevel median filtering method separately for filtering. Adaptive classification algorithm can not only remove the image noise, but also give the edge details better protection.We can obtain a complete root image through mosaicing local minirhizotron image, and then obtain a more comprehensive morphological distribution. This study presents the phase correlation and feature point matching for image mosaic. The improved Harris corner detection algorithm improves the Gray’s sensitivity and positioning accuracy of image; the improved corner response function helps to solve the random setting of K value of original function. According to the processing results of forward image, system can set threshold T of the follow-up image corner response function automatically, and can regulate brightness of the completed matching image.This article also did research on root edge feature extraction algorithm of the joined image. We chose cubic B-spline wavelet function according to Canny three guidelines for adaptive threshold multi scale image edge feature extraction of root, and obtain accurate root edge characteristic image based on multiple image data fusion after detected.We get root morphological distribution and parameter measurement through open and close operation of mathematical morphology. The study uses dilation, erosion and other techniques to extract the root edge feature like burring, depression, discontinuous and isolated holes, thins the root edge through mathematical morphology thinning operations, and provides data sources for the following root morphology parameters measurements. According to the linear relationship between the image pixels and actual size, and geometric properties of root morphological parameters, we can get the morphological parameters measurement like root length, surface area, mean diameter, root volume and angle.Through enhancement, denoising, image mosaic, root edge feature extraction and morphological measurement parameters of minirhizotron root image, we can obtain accurate acquisition and measurement of the root image, and provide detailed and accurate data foundation for research on plant root remodeling, subsequent soil reinforcement mechanism, carbon sequestration effect, and forecast.