Automated image processing framework for analysis of the density of fruiting bodies of Leptosphaeria maculans on oilseed rape stems

Abstract

Understanding the transmission of plant pathogen inoculum during the periods when the host plants are not present is crucial for predicting the initiation of epidemics and optimizing mitigation strategies. However, inoculum production at the end of the cropping season, survival during the intercrop period, and the emergence or release of inoculum can be highly variable, difficult to assess, and generally inferred indirectly from symptom data. As a result, a lack of large datasets hampers the study of these epidemiological processes. Here, inoculum production was studied in Leptosphaeria maculans, the cause of phoma stem canker of oilseed rape. The fungus survives on stubble left in the field, from which ascospores are released at the beginning of the next cropping season. An image processing framework was developed to estimate the density of fruiting bodies produced on stem pieces following incubation in field conditions, and a quality assessment of the processing chain was performed. A total of 2540 standardized RGB digital images of stems were then analysed, collected from 27 oilseed rape fields in Brittany over four cropping seasons. Manual post‐processing removed 16% of the pictures, e.g. when moisture‐induced darkening of the oilseed rape stems caused overestimation of the area covered with fruiting bodies. The potential level of inoculum increased with increasing phoma stem canker severity at harvest, and depended on the source field and the cropping season. This work shows how image‐based phenotyping generates high‐throughput disease data, opening up the prospect of substantially increased precision in epidemiological studies.

Plant phenotyping relevance

植物茎上の病原菌果実体密度を画像から推定する処理フレームワークを開発し、処理系の品質評価も行っており、植物病害状態の取得手法が研究の中心である。

Code and data availability

The paper describes an image-processing pipeline and phenotype data (fruiting body segmentation on 2094 oilseed rape stems), but no public repository, dataset, or code URL is given. The only availability statement is 'Source code is available upon request,' so the authors' analysis code qualifies only as a request-only

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