Improving Common Bacterial Blight Phenotyping by Using Rub Inoculation and Machine Learning: Cheaper, Better, Faster, Stronger

Abstract

Accurate assessment of plant symptoms plays a key role for measuring the impact of pathogens during plant−pathogen interaction. Common bacterial blight caused by Xanthomonas phaseoli pv. phaseoli and X. citri pv. fuscans is a major threat to common bean. The pathogenicity of these bacteria is variable among strains and depends mainly on a type III secretion system and associated type III effectors such as transcription activator-like effectors. Because the impact of a single gene is often small and difficult to detect, a discriminating methodology is required to distinguish the slight phenotype changes induced during the progression of the disease. Here, we compared two different inoculation and symptom assessment methods for their ability to distinguish two tal mutants from their corresponding wild-type strains. Interestingly, rub inoculation of the first leaves combined with symptom assessment by machine learning-based imaging allowed significant distinction between wild-type and mutant strains. By contrast, dip inoculation of first-trifoliate leaves combined with chlorophyll fluorescence imaging did not differentiate the strains. Furthermore, the new method developed here led to the miniaturization of pathogenicity tests and significant time savings.

Plant phenotyping relevance

機械学習画像による病徴評価法を開発・比較検証し、病原性試験の小型化と時間短縮にもつなげており、植物病害表現型の取得法が研究の中心です。

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