Stages-based multimodal data fusion model(S-MDFM) for wheat yield prediction and screening of drought-resistant and high-yield varieties

Abstract

Efficient and high-throughput prediction of crop yield and accurate assessment of varietal drought tolerance are essential for modern precision breeding and agricultural resource management and optimization. In this study, we propose a Stages-based Multimodal Data Fusion Model (S-MDFM) by integrating low-cost, high-throughput UAV-based multimodal imagery and multivariate data extracted from images. A staged error metrics (SEMs) propagation mechanism is constructed to capture the dynamic characteristics across growth stages and their dependencies with final yield, thereby improving the accuracy of cross-stage yield prediction (R2 = 0.8536, rRMSE = 16.12 %), validated prediction accuracy using data from the subsequent year in the same cropping area (R2 = 0.8370, rRMSE = 16.63 %). Based on wheat yield under varying water treatment gradients in drought stress experiments, the Drought Stress Tolerance Index (DSTI) and Drought Stress Susceptibility Index (DSSI) were employed to construct a Drought Resistance Index Differential (DRID) evaluation system. This dual-index approach enables multi-level screening of wheat varieties for drought resistance and quantitatively captures the synergistic relationship between yield performance and water adaptability and is capable of performing multi-level screening and quantifying the synergistic relationship between yield performance and water adaptability in wheat varieties, with an identification rate of drought-tolerant and high-yielding cultivars reaching 83.3 %. A multimodal fusion strategy based on discontinuous time-phase observations provides technical support for yield assessment and precise identification of drought-resistant and high-yielding varieties of wheat at different fertility stages, and the method provides a scalable multimodal data integration framework for varietal selection and breeding under drought-stressed field conditions, which is of great practical value for precision agriculture breeding applications.

Plant phenotyping relevance

UAVマルチモーダル画像から画像特徴を抽出し、段階的データ融合モデルでコムギ収量を予測し、乾燥耐性品種を定量スクリーニングする手法が研究の中心であり、翌年データによる検証も行っている。

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