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

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 selfattention 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 resourceconstrained mobile devices,facilitating portable plant phenotyping and smart breeding.