Automatic Tandem Dual BlendMask Networks for Severity Assessment of Wheat Fusarium Head Blight

Abstract

Fusarium head blight (FHB) disease reduces wheat yield and quality. Breeding wheat varieties with resistance genes is an effective way to reduce the impact of this disease. This requires trained experts to assess the disease resistance of hundreds of wheat lines in the field. Manual evaluation methods are time-consuming and labor-intensive. The evaluation results are greatly affected by human factors. Traditional machine learning methods are only suitable for small-scale datasets. Intelligent and accurate assessment of FHB severity could significantly facilitate rapid screening of resistant lines. In this study, the automatic tandem dual BlendMask deep learning framework was used to simultaneously segment the wheat spikes and diseased areas to enable the rapid detection of the disease severity. The feature pyramid network (FPN), based on the ResNet-50 network, was used as the backbone of BlendMask for feature extraction. The model exhibited positive performance in the segmentation of wheat spikes with precision, recall, and MIoU (mean intersection over union) values of 85.36%, 75.58%, and 56.21%, respectively, and the segmentation of diseased areas with precision, recall, and MIoU values of 78.16%, 79.46%, and 55.34%, respectively. The final recognition accuracies of the model for wheat spikes and diseased areas were 85.56% and 99.32%, respectively. The disease severity was obtained from the ratio of the diseased area to the spike area. The average accuracy for FHB severity classification reached 91.80%, with the average F1-score of 92.22%. This study demonstrated the great advantage of a tandem dual BlendMask network in intelligent screening of resistant wheat lines.

Keywords

• deep learning
• wheat spike
• Fusarium head blight
• object recognition
• image segmentation