摘要Rice blast and brown spot are two of the most significant fungal diseases affecting rice production.Although the leaf symptoms of both diseases are distinct,they manifest as similar brown-black lesions,complicating differ-entiation and effective management.Traditional diagnostic methods predominantly rely on DNA-based molecular techniques,which are not well-suited for rapid,large-scale applications.This study aimed to identify reliable chlorophyll fluorescence(ChlF)indicators for diagnosing these diseases at the pre-symptomatic stage using a pulse-amplitude modulation fluorometer.Changes in ChlF parameters were measured following fungal infection in 120 leaves and 750 spots across five time points in detached leaf assays.Diagnostic indicators were selected through machine learning and fold-change value comparisons,then validated using 374 pre-symptomatic spots induced under different infection conditions in whole plants.Fifteen ChlF diagnostic parameters were identified,nine of which were specifically associated with rice blast.Pre-symptomatic lesions in both diseases caused sig-nificant decreases in non-photochemical quenching parameters(NPQ_D1/D2 and qN_D1/D2/D3),while simul-taneous increases in photochemical quenching parameters(qL_L2/L3,qP_L2/L3/L4,and QY_L2/L3/L4)were observed only in rice blast.The vitality index Rfd_L2 exhibited a notable increase in both diseases.However,a consistent decrease in Ft_L2,necessary for calculating Rfd_L2,was observed only in rice blast,indicating different pathogenic mechanisms despite similar visible symptoms.Our results suggest that these ChlF parameters can serve as powerful diagnostic indicators for rice fungal diseases,and that ChlF imaging offers a straightforward approach for plant disease diagnosis.