Early detection of cotton verticillium wilt based on root magnetic resonance images.

Abstract

Verticillium wilt (VW) is often referred to as the cancer of cotton and it has a detrimental effect on cotton yield and quality. Since the root system is the first to be infested, it is feasible to detect VW by root analysis in the early stages of the disease. In recent years, with the update of computing equipment and the emergence of large-scale high-quality data sets, deep learning has achieved remarkable results in computer vision tasks. However, in some specific areas, such as cotton root MRI image task processing, it will bring some challenges. For example, the data imbalance problem (there is a serious imbalance between the cotton root and the background in the segmentation task) makes it difficult for existing algorithms to segment the target. In this paper, we proposed two new methods to solve these problems. The effectiveness of the algorithms was verified by experimental results. The results showed that the new segmentation model improved the Dice and mIoU by 46% and 44% compared with the original model. And this model could segment MRI images of rapeseed root cross-sections well with good robustness and scalability. The new classification model improved the accuracy by 34.9% over the original model. The recall score and F1 score increased by 59% and 42%, respectively. The results of this paper indicate that MRI and deep learning have the potential for non-destructive early detection of VW diseases in cotton.

Plant phenotyping relevance

綿花根のMRI画像から病害状態を検出・分節する画像解析手法を開発し、性能検証しており、植物フェノタイピング手法が中心である。

Code and data availability

The paper's cotton root MRI dataset (1191 images) and trained MRSwinUNet/MRResNet models are paper-specific phenotyping assets, but no public repository or download URL is provided. The data availability statement only offers the raw data from the authors upon request; the supplementary material link is on the article页

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