From Canopy Images to Organ-Level Disease Assessments: A Scalable Approach to Measure Quantitative Resistance in the Field

Abstract

Breeding for quantitative, polygenic resistance is widely considered the most durable, cost-effective, and environmentally safe approach to crop disease control. However, progress in resistance breeding is hindered by the limited capability of current approaches to measure highly quantitative disease phenotypes under field conditions with high precision and sufficient throughput. Here, we present an imaging protocol and a modular image processing pipeline that enables wheat disease detection and severity estimation directly from very-high-resolution canopy imagery, eliminating the need for physical interaction with the monitored plants as required in previously proposed sensor-based methods capable of symptom-level diagnosis. The pipeline combines deep-learning-based semantic segmentation, keypoint detection, and depth estimation to diagnose and quantify disease symptoms and extract the analyzable reference plant surfaces for severity estimation. By leveraging estimated relative depth and analyzing image texture, well-focused areas with sufficient quality were accurately segmented. Despite the challenging nature of canopy images and frequent symptom ambiguity, symptom detection and segmentation models trained on a new dataset reached a similar performance as already described in more simplified scenarios where detached, flattened leaves were analyzed. Plot-level severity estimates of Septoria Tritici Blotch, a major wheat disease, obtained using the new method and a precise but more laborious reference method were highly correlated (Pearson R = 0.83) across a range of morphologically contrasting cultivars. Validation of the new method on data collected by different operators at different sites demonstrated the robustness of the approach. The ability of the method to process imagery acquired in a contact-free manner can enable deployment on autonomous ground vehicles, paving the way for automated, scalable phenotype acquisition.

Plant phenotyping relevance

圃場キャノピー画像からコムギ病徴を検出・重症度推定する画像処理パイプラインを開発し、基準法との相関および異なる操作者・地点での頑健性を検証しており、植物表現型取得法が中心です。

Code and data availability

The supplied blocks describe newly created datasets (EFDv2, OSD, LIAC) and a deep-learning pipeline for wheat STB phenotyping, but contain no explicit public availability statement, repository, or URL for the datasets, code, or trained models. No paper-specific public asset is actionable from the provided text.

No evidence-backed public reproduction asset is currently recorded.