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To develop an automated system for quantifying spore germination in Fusarium head blight using a deep learning framework.

January 6, 2026AgricultureOpen Access

EffiFormer-CGS: Deep Learning Framework for Automated Quantification of Fusarium Spore Germination

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To develop an automated system for quantifying spore germination in Fusarium head blight using a deep learning framework.
Implemented a three-module deep learning framework: object detection, key point localization, and phenotypic quantification.
Created a dataset of 2381 annotated images of Fusarium spores exposed to triazole fungicides.
Calculated germination degree based on the ratio of germ tube length to body length.
Achieved 90.8% mean average precision (mAP) in object detection.
Attained 91.4% mAP in key point localization.
All tested fungicides significantly inhibited spore germination, with Tebuconazole as one of the agents used.

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Deep learning framework automates spore germination assessment in Fusarium using microscopic images, highlighting Tebuconazole's effects.

Wang et al. (2026) studied this question.

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