Plant root image processing and analysis based on 2D scanner

Chen, Yugan; Zhou, Xuecheng

doi:10.1109/bicta.2010.5645146

Cited by 3 publications

(1 citation statement)

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“…A segmentation method based on color features of the roots of wheat seedlings was implemented: firstly, the root image was converted from RGB color space to HCI color space, then the threshold of the chroma component was set to extract the binary image, and finally, the image was processed with local fuzzy c-means clustering algorithm to get the segmentation result ( Goclawski et al., 2009 ). The OTSU method was applied to the study of automatic root segmentation of the images acquired by desktop scanners, and this method is an image segmentation algorithm based on a dynamic threshold ( Chen and Zhou, 2010 ). The threshold segmentation methods are usually only applicable to the automatic segmentation of root images with simple backgrounds.…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of cotton roots in high-resolution minirhizotron images based on improved OCRNet

et al. 2023

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Root phenotypic parameters are the important basis for studying the growth state of plants, and root researchers obtain root phenotypic parameters mainly by analyzing root images. With the development of image processing technology, automatic analysis of root phenotypic parameters has become possible. And the automatic segmentation of roots in images is the basis for the automatic analysis of root phenotypic parameters. We collected high-resolution images of cotton roots in a real soil environment using minirhizotrons. The background noise of the minirhizotron images is extremely complex and affects the accuracy of the automatic segmentation of the roots. In order to reduce the influence of the background noise, we improved OCRNet by adding a Global Attention Mechanism (GAM) module to OCRNet to enhance the focus of the model on the root targets. The improved OCRNet model in this paper achieved automatic segmentation of roots in the soil and performed well in the root segmentation of the high-resolution minirhizotron images, achieving an accuracy of 0.9866, a recall of 0.9419, a precision of 0.8887, an F1 score of 0.9146 and an Intersection over Union (IoU) of 0.8426. The method provided a new approach to automatic and accurate root segmentation of high-resolution minirhizotron images.

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Section: Introductionmentioning

confidence: 99%