Abstract
Wheat stripe rusts are responsible for the major reduction in production and economic losses in the wheat industry. Thus, accurate detection of wheat stripe rust is critical to improving wheat quality and the agricultural economy. At present, the results of existing wheat stripe rust detection methods based on convolutional neural network (CNN) are not satisfactory due to the arbitrary orientation of wheat stripe rust, with a large aspect ratio. To address these problems, a WSRD-Net method based on CNN for detecting wheat stripe rust is developed in this study. The model is a refined single-stage rotation detector based on the RetinaNet, by adding the feature refinement module (FRM) into the rotation RetinaNet network to solve the problem of feature misalignment of wheat stripe rust with a large aspect ratio. Furthermore, we have built an oriented annotation dataset of in-field wheat stripe rust images, called the wheat stripe rust dataset 2021 (WSRD2021). The performance of WSRD-Net is compared to that of the state-of-the-art oriented object detection models, and results show that WSRD-Net can obtain 60.8% AP and 73.8% Recall on the wheat stripe rust dataset, higher than the other four oriented object detection models. Furthermore, through the comparison with horizontal object detection models, it is found that WSRD-Net outperforms horizontal object detection models on localization for corresponding disease areas.
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Claims 4
Claim · model-written summary
The study provides an oriented annotation dataset of field wheat stripe rust images.
- Taxon
- wheat
Triticum aestivume0 - Outcome
- oriented annotation dataset of in-field wheat stripe rust imagese0
- Setting
- in-field wheat stripe rust imagese0
Not reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“we have built an oriented annotation dataset of in-field wheat stripe rust images, called the wheat stripe rust dataset 2021 (WSRD2021)”
Claim · model-written summary
WSRD-Net achieved 60.8% average precision for wheat stripe rust detection, above four oriented detectors.
- Taxon
- WheatTriticum aestivumt0
- Outcome
- APe0
- Direction
- highere0
- Value
- 60.8%e0
- Comparator
- other four oriented object detection modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“WSRD-Net can obtain 60.8% AP and 73.8% Recall on the wheat stripe rust dataset, higher than the other four oriented object detection models”
t0“Wheat Stripe Rust”
Claim · model-written summary
WSRD-Net achieved 73.8% recall for wheat stripe rust detection, above four oriented detectors.
- Taxon
- WheatTriticum aestivumt0
- Outcome
- Recalle0
- Direction
- highere0
- Value
- 73.8%e0
- Comparator
- other four oriented object detection modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“WSRD-Net can obtain 60.8% AP and 73.8% Recall on the wheat stripe rust dataset, higher than the other four oriented object detection models”
t0“Wheat Stripe Rust”
Claim · model-written summary
WSRD-Net localized wheat stripe rust disease areas better than horizontal object detectors.
- Taxon
- WheatTriticum aestivumt0
- Outcome
- localization for corresponding disease arease0
- Direction
- outperformse0
- Comparator
- horizontal object detection modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“WSRD-Net outperforms horizontal object detection models on localization for corresponding disease areas”
t0“Wheat Stripe Rust”