FHBDSR-Net: automated measurement of diseased spikelet rate of Fusarium Head Blight on wheat spikes.

Wu, Ze and Zhao, Haowei and Chen, Zeyu and Suo, Yongqiang and Joseph, Seena and Yuan, Xiaohui and Lan, Caixia and Liu, Weizhen (2025) FHBDSR-Net: automated measurement of diseased spikelet rate of Fusarium Head Blight on wheat spikes. aBIOTECH, 6 (4). pp. 726-743. ISSN 2096-6326

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Abstract

Fusarium Head Blight (FHB), a fungal wheat (Triticum aestivum) disease that threatens global food security, requires precise quantification of diseased spikelet rate (DSR) as a phenotypic indicator for resistance breeding. Most techniques for measuring DSR rely on manual spikelet-by-spikelet observation and counting, which is inefficient and destructive. Although deep learning offers great promise for automated DSR measurement, existing intelligent detection algorithms are hampered by the lack of spikelet-level annotated data, insufficient feature representation for diseased spikelets, and weak spatial encoding of densely arranged spikelets. To address these challenges, we constructed a dataset of 620 high-resolution RGB images of wheat spikes with 5,222 spikelet-level annotations to systematically analyze spikelet size distributions to fill small-object detection data gaps in this field. We designed FHBDSR-Net, a light framework for automated DSR measurement centered on diseased spikelet detection, which features (1) multi-scale feature enhancement architecture that dynamically combines lesion textures, morphological features, and lesion-awn contrast through adaptive multi-scale kernels to suppress background noise; (2) the Inner-EfficiCIoU loss function to reduce small-target localization errors in dense contexts; and (3) a scale-aware attention module using dilated convolutions and self-attention to encode multi-scale pathological patterns and spatial distributions to enhance dense spikelet resolution. FHBDSR-Net detected diseased spikelets with an average precision of 93.8% with a lightweight design of 7.2 M parameters. The results were strongly correlated with expert evaluations, with a Pearson correlation coefficient of 0.901. Our method is suitable for deployment on resource-constrained mobile devices, facilitating portable plant phenotyping and smart breeding.

Item Type: Article
Additional Information: ** From Europe PMC via Jisc Publications Router ** History: epub 02-09-2025; ppub 01-12-2025. ** Licence for this article: cc by
Uncontrolled Keywords: Object Detection, Wheat Fusarium Head Blight, Diseased Spikelet Rate, Smart Breeding
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
S Agriculture > SB Plant culture
Divisions: Institutes and Academies > Wales Institute for Science & Art (WISA) > Academic Discipline: Applied Computing
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Identification Number: https://doi.org/10.1007/s42994-025-00250-3
SWORD Depositor: JISC Publications Router
Depositing User: JISC Publications Router
Date Deposited: 09 Dec 2025 09:52
Last Modified: 09 Dec 2025 09:56
URI: https://repository.uwtsd.ac.uk/id/eprint/4035

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