LIME: a fully automated pipeline for high-throughput quantification of leaf lesions

Abstract

Abstract Accurate quantification of leaf lesion severity is essential for plant disease research and phenotyping but is often limited by subjective visual scoring and time-intensive manual image analysis. We present LIME, a fully automated, open-source image analysis pipeline for high-throughput quantification of leaf lesions from disease assay images. LIME integrates zero-shot leaf segmentation using the Segment Anything Model with a convolutional neural network for lesion area estimation. Applied to Arabidopsis thaliana leaves infected with Sclerotinia sclerotiorum , the proposed approach achieved a mean absolute percentage error of 12.9%, comparable to observed intrarater variability in manual scoring. Stratified evaluation across lesion-size groups demonstrated consistent prediction accuracy for small, intermediate, and large lesions, and comparative analysis showed that the deep learning–based model substantially outperformed color-based baseline methods. Under GPU-accelerated execution, LIME processed complete assays containing approximately 200 leaves in 15 minutes, representing an approximate 13-fold reduction in processing time relative to manual annotation. Together, these results indicate that LIME enables objective, reproducible, and scalable quantification of leaf lesion severity in standardized plant pathology assays. The pipeline is released as an open-source tool to support quantitative phenotyping studies.

Plant phenotyping relevance

植物病斑重症度を画像から定量するオープンソース解析パイプラインを開発・比較評価しており、植物フェノタイピング手法が研究の中心です。

Code and data availability

The paper describes an open-source pipeline (LIME), a 1,700-image dataset of Sclerotinia-infected Arabidopsis leaves with ImageJ ground-truth annotations, and a trained NASNet A model, but the supplied blocks contain no public repository URL, deposit identifier, or availability statement pointing to a concrete public,

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