RGB imaging-based detection of rice leaf blast spot and resistance evaluation at the canopy scale
Visual inspection of rice leaf blast resistance is time-consuming and labor-intensive with low accuracy. Therefore, this study aims to identify and detect rice leaf blast spots based on RGB imaging of rice canopy combined with mask regions with convolutional neural network (Mask-RCNN), and develop m...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Zhejiang University Press
2021-08-01
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| Series: | 浙江大学学报. 农业与生命科学版 |
| Subjects: | |
| Online Access: | https://www.academax.com/doi/10.3785/j.issn.1008-9209.2021.05.131 |
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| Summary: | Visual inspection of rice leaf blast resistance is time-consuming and labor-intensive with low accuracy. Therefore, this study aims to identify and detect rice leaf blast spots based on RGB imaging of rice canopy combined with mask regions with convolutional neural network (Mask-RCNN), and develop multiple classification models to quantify the number of disease spots and evaluate the association between the number of disease spots and the resistance level by analyzing the quantitative information of different categories of disease spots in RGB images of rice. First, we collected RGB images from different rice breeding lines at the seedling stage, including japonica lines, early indica lines and indica recovery lines. Preprocessing and labeling of the input images were then performed. A Mask-RCNN model for the recognition of rice leaf blast spots was developed to perform the rectangular frame detection, mask segmentation and classification. The classification result of rice leaf blast spots with the mean intersection over union (mIoU) of 0.603 was achieved. The mean average precision (mAP) of the test dataset was 0.716, when the intersection over union (IoU) threshold of 0.5 was used. Among all the classification models, Gaussian process support vector machine obtained the highest prediction accuracy of 94.30% (proportion of disease spots in each category corresponding to different resistances) on the test dataset. The above results demonstrate that RGB images of rice canopy combined with Mask-RCNN have the great potential for the accurate identification of rice leaf blast spots, and the number of detected disease spots is highly correlated with the rice leaf blast resistance level. The proposed method is promising for efficient selection of disease-resistant rice varieties in breeding. |
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| ISSN: | 1008-9209 2097-5155 |