Claim · model-written summary
WSRD-Net localized wheat stripe rust disease areas better than horizontal object detectors.
- Taxon
- Wheat
Triticum aestivumt0 - Outcome
- localization for corresponding disease arease0
- Direction
- outperformse0
- Comparator
- horizontal object detection modelse0
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingValueQualifier
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”
Title
WSRD-Net: A Convolutional Neural Network-Based Arbitrary-Oriented Wheat Stripe Rust Detection Method
Haiyun Liu; Lin Jiao; Rujing Wang; et al. Frontiers in Plant Science 2022. doi:10.3389/fpls.2022.876069
Other claims from this paper (3)
- Method / resource The study provides an oriented annotation dataset of field wheat stripe rust images.
- Method / resource WSRD-Net achieved 60.8% average precision for wheat stripe rust detection, above four oriented detectors.
- Method / resource WSRD-Net achieved 73.8% recall for wheat stripe rust detection, above four oriented detectors.