Estimating wheat grain filling course by fusing digital and thermal infrared images

Abstract

Grain filling plays a vital role in determining both the yield and quality of wheat. Therefore, timely and accurate monitoring of the grain filling course (GFC) is essential for assessing the feasibility of harvest timing optimization. Traditional methods based on field sampling are time-consuming and destructive. This study presents a non-destructive method for estimating the wheat grain filling course (GFC) by integrating ground-based RGB and thermal infrared imagery. Wheat ears were first segmented using a temperature-threshold approach, after which colour and temperature features were extracted. Grain water content (GWC) was then estimated using a Normalised Relative Ear Temperature (NRET) index, while days after anthesis (DAA) were retrieved using a piecewise linear model derived from ear colour features. Finally, a grain filling index (Kf) was developed using DAA corresponding to 25 % moisture content (DAA25%) to quantify the GFC. Results showed that thermal images acquired at 17:00 showed the greatest separability between ears and background canopy and the highest sensitivity to irrigation differences. Both NRET and DAA based models provided accurate GWC estimates (R² = 0.86 and 0.91; RMSE = 3.13 % and 4.21 %; rRMSE = 0.07 and 0.09, respectively). The Kf index effectively captured differences in GFC under different irrigation treatments and detected early maturity under water stress (p < 0.05). This study demonstrates the potential of combining thermal and RGB imagery for high-resolution, non-destructive monitoring of wheat grain filling and for supporting timely harvest management.

Plant phenotyping relevance

RGB画像と熱赤外画像を統合し、穂の分割・特徴抽出から穀粒水分含量と登熟進行を推定する非破壊フェノタイピング手法が研究の中心である。

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