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

TL;DR

This study demonstrates that combining sun-induced chlorophyll fluorescence with reflectance data significantly improves the accuracy of estimating rice photosynthetic capacity, advancing high-throughput crop phenotyping methods.

Contribution

It introduces a data fusion approach at multiple levels that enhances prediction accuracy of photosynthetic traits in rice using SIF and reflectance spectra.

Findings

Data fusion at measurement, feature, and decision levels improves prediction accuracy.

Decision-level fusion yields the best results among the fusion strategies.

Sun-induced chlorophyll fluorescence effectively predicts rice photosynthetic capacity.

Abstract

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