The findings offer a non-invasive and efficient method for early disease detection, crucial for timely intervention and better disease management.
Rice is a vital global food staple, contributing significantly to daily calorie intake. However, up to 30% of annual rice yield is lost due to diseases, with rice blast and brown spot being among the most destructive fungal diseases. Accurate early detection of these diseases is challenging due to the similarity in their visual symptoms at the pre-symptomatic stage. Traditional molecular methods, while precise, are time-consuming and not easily scalable for field applications. In contrast, ChlF imaging, which measures the light emitted by chlorophyll molecules during photosynthesis, has emerged as a promising tool for detecting early plant stress before visible symptoms appear. This study aimed to harness ChlF imaging for distinguishing rice blast and brown spot at the early stages of infection.
A study (DOI: 10.1016/j.plaphe.2025.100012) published in Plant Phenomics on 15 February 2025 by Jae Hoon Lee’s team, Seoul National University, provides a powerful, non-destructive tool for diagnosing rice fungal diseases at the pre-symptomatic stage.
The study employed pulse-amplitude modulation (PAM) ChlF imaging to monitor changes in rice leaves infected with rice blast and brown spot. A total of 120 leaves and 750 spots were analyzed across five time points in detached leaf assays. The experimental setup involved treating rice leaves with conidial suspensions of Magnaporthe oryzae and Cochliobolus miyabeanus at various concentrations. The ideal concentrations of 5 × 10^4 for M. oryzae and 1 × 10^3 for C. miyabeanus were selected, as they induced individual lesions while maintaining comparable disease severity. Disease progression was categorized into asymptomatic, pre-symptomatic, and symptomatic stages based on lesion appearance, with visual inspection confirming the distinct patterns for each disease. ChlF images were taken at different time points, and 98 ChlF parameters were analyzed using principal component analysis (PCA), revealing a divergence in ChlF patterns between healthy and infected leaves, particularly during the pre-symptomatic stage. Significant increases in photochemical quenching parameters were noted in both diseases, with rice blast showing unique decreases in non-photochemical quenching (NPQ) and qN parameters, which were not observed in brown spot. Machine learning techniques were employed to classify ChlF data, achieving high classification accuracies (over 92%) at both leaf and lesion levels. The study identified several key ChlF parameters, including Rfd_L2, QY_Lss, and qP_Lss, as reliable diagnostic indicators for early disease detection. Validation through whole-plant assays confirmed the efficacy of these parameters, with rice blast-specific indicators showing distinct patterns compared to brown spot. These findings suggest that ChlF imaging is a powerful tool for early, non-invasive detection of rice fungal diseases, facilitating timely disease management and intervention.
This study underscores the potential of ChlF imaging as an early diagnostic tool for rice fungal diseases, offering a non-invasive, scalable, and accurate method for disease detection. By identifying distinct ChlF signatures for rice blast and brown spot, the research paves the way for more efficient disease management practices, enhancing rice production and contributing to global food security.
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References
DOI
10.1016/j.plaphe.2025.100012
Original Source URL
https://doi.org/10.1016/j.plaphe.2025.100012
Funding information
This work was supported by Rural Development Administration of Korea (RS-2022-RD010059) and Creative-Pioneering Researchers Program by Seoul National University.
About Plant Phenomics
Science Partner Journal Plant Phenomics is an online-only Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and distributed by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal's Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.