A high-throughput extraction method for anatomical feature parameters of stem tissue based on deep learning

Abstract

The present invention proposes a high-throughput extraction method for anatomical feature parameters of stem tissue based on deep learning, which includes the following steps: S1, draw contours and label categories for digital slice images; S2, use ResNet as a feature extractor to extract the original The image is mapped to a high-dimensional feature space, and then output feature maps at multiple levels; S3, uses a feature enhancer of the encoder-decoder structure to extract high- and low-order feature maps and fuse them to obtain the strongest feature; S4, in order to identify instances in the same semantic category, then use a branch detection method to indicate different instances; the branch detection method includes using category branch, width and height branch and regression branch to generate detection output; S5, after the detection output , using an instance mask to predict the contours of vascular bundles and a semantic mask to represent the three functional regions of the maize stalk cross-section. The present invention can analyze and detect corn stalk cross-section slice image phenotype parameters with high accuracy and robustness.

Description

technical field [0001] The invention relates to the field of deep learning, in particular to a high-throughput extraction method for anatomical characteristic parameters of stem tissue based on deep learning. Background technique [0002] Microscopic phenotypic features in crop stems, especially vascular bundles and functional domains, are closely related to nutrient transport and lodging resistance in crops. Quantitative analysis of anatomical features using crop cross-sectional slice images is a great challenge for scientists in plant science. Therefore, there is an urgent need to develop an accurate and high-throughput phenotyping method. Traditional image processing methods are not suitable for processing uncontrollable but non-negligible variable factors in maize stalk section images, and high-throughput methods based on micro-CT technology are expensive, which limits their application. Deep learning methods simulate the human eye to observe, learn features of objects...

AI Technical Summary

Problems solved by technology

However, there are few reports of deep learning-based methods for solving the problem of maize stalk phenotype

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