XooNet: a high-throughput UAV-based approach for field screening of bacterial blight-resistant germplasm in wild rice.

Abstract

Bacterial blight (BB) poses a significant threat to rice production, necessitating efficient screening of resistant wild rice germplasm to facilitate breeding. Traditional methods are labor-intensive and subjective, while existing UAV-based approaches suffer from high costs or incomplete solutions. This study introduces XooNet, a novel UAV-based method for automated BB resistance screening in wild rice, which classifies wild rice into several levels based on BB resistance. To facilitate this method, a high-precision and lightweight oriented bounding box (OBB) detection algorithm for BB in wild rice has been developed. Experimental results show that the screening method achieved an accuracy of 97.5%. After applying the LAMP pruning strategy to balance performance and efficiency, the detection model achieved an accuracy of 93.1% with a significantly reduced parameter size of 1.4M and a computational complexity of 3.5 GFLOPs. This approach will facilitate the high-throughput screening of extensive wild rice germplasm for BB resistance, thereby expediting the discovery of valuable wild rice genetic resources.

Code and data availability

The supplied blocks describe a UAV image dataset (2,035 images, 12,210 augmented crops) and a YOLOv11-OBB-based detection model (XooNet), but no block contains a data availability statement, repository deposit, or authors' public URL for the dataset, annotations, code, or trained model. No paper-specific public asset,

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