The dataset has been deposited in the open access Jülich DATA reposi ease using UAV-supported image data and deep learning. Sugar Industry tory: https://doi.org/10.26165/JUELICH-DATA/FOQOFI. 147, 79–86. Ispizua Yamati FR, Bömer J, Noack N, Linkugel T, Paulus S, Mahlein A-K. 2025. Configuration of a multisensor platform for advanced plant phe References notyping and disease detection: case study on cercospora leaf spot in sugar Ač A, Malenovský Z, Olejníč ková J, Gallé A, Rascher U, Mohammed beet. Smart Agricult
Open resource ↗Jülich DATA · 10.26165/JUELICH-DATA/FOQOFI · pdf-layout-page:14 lines:52-72Unverified paper record
Sun-induced fluorescence responses to structural and physiological effects caused by the Cercospora leaf spot in sugar beet
Journal of Experimental Botany · 18 Mar 2026 · 10.1093/jxb/erag144
Abstract
Sun-induced fluorescence (SIF) has emerged as a promising tool for tracking photosynthetic dynamics, yet its application in monitoring biotic stress remains underexplored in field conditions. In this study, we investigated the effects of Cercospora leaf spot (CLS), a destructive foliar disease of sugar beet (Beta vulgaris L.), for which traditional monitoring methods often fail to capture subtle disease effects or distinguish between structural and physiological stress responses. CLS infection was induced through artificial inoculation and manually scored. Canopy-level reflectance indices were acquired along with red and far-red passive SIF signals and active PSII efficiency traits using FloX and LIFT sensors mounted on an automated high-throughput phenotyping platform. The results demonstrate that SIF effectively detects CLS in sugar beet, with responses comparable with structural and disease- specific indices. Despite visible symptoms, PSII efficiency (Fq'/Fm') remained stable across treatments, indicating limited impairment of leaf photosynthetic efficiency at early stages. However, the canopy-level electron transport rate varied significantly and showed a strong relationship with red and far-red SIF, suggesting that CLS primarily affects canopy light absorption and utilization. After structural normalization, SIF yield remained largely unchanged, confirming that observed SIF reductions were mainly driven by canopy structural alterations. Overall the study demonstrates the effectiveness of SIF for large-scale disease monitoring and integration into high-throughput phenotyping, while also revealing structural and physiological factors influencing the SIF signal under disease stress.
Plant phenotyping relevance
SIFおよびPSIIセンサーを搭載したハイスループット表現型解析プラットフォームで、サトウダイコンの病害状態と構造・生理応答を評価する手法の実質的な適用・検証が中心である。
abstractCanopy-level reflectance indices were acquired along with red and far-red passive SIF signals and active PSII efficiency traits using FloX and LIFT sensors mounted on an automated high-throughput phenotyping platform.
abstractThe results demonstrate that SIF effectively detects CLS in sugar beet
abstractOverall the study demonstrates the effectiveness of SIF for large-scale disease monitoring and integration into high-throughput phenotyping
Code and data availability
The paper's phenotyping dataset (SIF, reflectance indices, LIFT PSII traits, disease scores from the CLS sugar beet field trial) is deposited in the open access Jülich DATA repository under DOI 10.26165/JUELICH-DATA/FOQOFI. No separate author analysis code repository with explicit availability language is stated; R/lme
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