FHBDSR-Net: AI tool enables accurate measurement of diseased spikelet rate of wheat Fusarium Head Blight from phone images, aiding smart phenotyping

This study is led by Professor Weizhen Liu (School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China). The authors proposed a deep learning algorithm named FHBDSR-Net, which can automatically measure the diseased spikelet rate (DSR) trait from wheat spike images with complex backgrounds captured by mobile phones, providing an efficient and accurate phenotypic measurement tool for wheat Fusarium Head Blight (FHB) resistance breeding.

The FHBDSR-Net model integrates three innovative modules: the Multi-scale Feature Enhancement (MFE) module effectively suppresses complex background interference by dynamically fusing lesion texture, morphological features, and lesion-awn contrast features; the Inner-Efficient CIoU (Inner-EfficiCIoU) loss function significantly improves the localization accuracy of dense small targets; the Scale-Aware Attention (SAA) module enhances the encoding capability of multi-scale pathological features and spatial distribution through dilated convolution and self-attention mechanisms.

Experimental results show that FHBDSR-Net achieves 93.8% average precision (AP) in diseased spikelet detection, with the average Pearson’s correlation coefficient between its DSR measurements and manual observations exceeding 0.901. The model possesses excellent generalization ability and robustness, and exhibits high accuracy in detecting the DSR of wheat spikes with different varieties, growth stages, and infection degrees. Meanwhile, FHBDSR-Net also features lightweight properties with only 7.2M parameters, which can be adapted for deployment on resource-constrained mobile terminals. It can support the accurate acquisition of DSR trait in greenhouse and field scenarios, further providing efficient and reliable technical support for wheat FHB resistance breeding screening and field disease dynamic monitoring, and promoting the upgrading of plant phenotyping analysis towards field portability and intelligence.

See the article:

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

https://link.springer.com/article/10.1007/s42994-025-00245-0