Grain yield prediction using multi-temporal UAV-based multispectral vegetation indices and endmember abundance in rice

Abstract

Timely and accurately predicting grain yield before harvest is of great importance for rice production management and grain trade. Numerous methods based on remote sensing (RS) technology have explored to estimate rice grain yield. However, in most cases, these methods are negatively affected by the mixed pixels of RS images due to the effect of background and panicles. To resolve such issues, the abundance information of leaves, soil and water background and rice panicles were extracted from the multispectral images of unmanned aerial vehicle (UAV) for the multiple endmember spectral mixture analysis (MESMA) method. Based on the analysis of contribution of vegetation index (VI) and abundance (ABD) to rice grain yield, a rice grain yield prediction model was developed by combining multi-stage time-series, ABD and VI. Results showed that MESMA can mitigate endmember variability in the estimation of rice yield, and achieve higher accuracy than conventional spectral mixture analysis (SMA). The NDRE and the sum of leaf and panicle abundance (ABDL₊P) can be well correlated to grain yield before heading stage (R² = 0.75) and at heading stage (R² = 0.72), respectively. In comparison to them, the multi-stage time-series model consisted of VI and ABD [∑(VI&ABD)] produces the highest correlation (R² = 0.80). It could also resolve the issues about the underestimation of yield using different datasets from various multi-spectral camera in comparison to the single-parameter models ∑VI and ∑ABD, and produce the good validation accuracy with R²=0.73, RRMSE=0.22 and R²=0.75, RRMSE=0.15, respectively. This study suggests that the combination of UAV multi-temporal VI and ABD data can achieve accurate prediction of rice grain yield. This method effectively utilizes the optical information of leaves and rice panicles and reduces background effects, which provides a new idea for accurate rice yield prediction.

Plant phenotyping relevance

UAVマルチスペクトル画像から葉・穂・背景の存在量を抽出し、イネの収量を推定する手法を開発・検証しており、表現型取得・推定法が研究の中心である。

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