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Studying crop photosynthesis is crucial for improving yield, but currentmethods are labor-intensive. This research aims to enhance accuracy bycombining leaf reflectance and sun-induced chlorophyll fluorescence (SIF)signals to estimate key photosynthetic traits in rice. The study analyzes 149leaf samples from two rice cultivars, considering reflectance, SIF,chlorophyll, carotenoids, and CO2 response curves. After noise removal, SIF andreflectance spectra are used for data fusion at different levels (raw, feature,and decision). Competitive adaptive reweighted sampling (CARS) extractsfeatures, and partial least squares regression (PLSR) builds regression models.Results indicate that using either reflectance or SIF alone provides modestestimations for photosynthetic traits. However, combining these data sourcesthrough measurement-level data fusion significantly improves accuracy, withmid-level and decision-level fusion also showing positive outcomes. Inparticular, decision-level fusion enhances predictive capabilities, suggestingthe potential for efficient crop phenotyping. Overall, sun-induced chlorophyllfluorescence spectra effectively predict rice's photosynthetic capacity, anddata fusion methods contribute to increased accuracy, paving the way forhigh-throughput crop phenotyping.

Investigation on data fusion of sun-induced chlorophyll fluorescence and reflectance for photosynthetic capacity of rice