UAV‐based deep transfer learning to improve grain yield prediction in winter wheat across temporal and spatial variability

Abstract

Abstract Accurate prediction of grain yield (GY) remains a major challenge in plant breeding due to complex interactions between genotype, environment, and management (G × E × M) factors. Remote sensing data from unmanned aerial vehicles (UAVs) equipped with multispectral sensors have emerged as a pivotal resource for high‐throughput phenotyping. In this study, we applied a deep transfer learning (DTL) approach to enhance GY prediction using UAV‐derived spectral and textural traits across multiple environments and developmental stages of winter wheat ( Triticum aestivum L.). The model's transferability and generalizability were evaluated across years, locations, nurseries, and stage‐specific scenarios. We employed fine‐tuning, which involves retraining a pretrained one‐dimensional convolutional neural network (1D‐CNN) model on scenario‐specific target data to enhance generalizability. Fine‐tuning was tested with 20%, 40%, 60%, and 80% of the target data to identify an optimal balance between model accuracy and adaptation, and compared with 1D‐CNN without DTL (baseline model). In cross‐year predictions, the baseline model (from 2022) performed poorly ( R 2 = −2.9 in 2023), while DTL improved prediction to an R 2 of 0.83 with 20% fine‐tuning, demonstrating strong temporal adaptability. Similarly, in cross‐location scenarios, baseline model performance was poor ( R 2 ranging from −15.3 to −0.4) but improved to 0.29–0.69 with 40% fine‐tuning. Stage‐specific predictions benefited most at Feekes stages 10.5 and 11, where DTL achieved an R 2 of 0.78 and 0.82, respectively, compared to baseline model R 2 values near 0. These results demonstrate that DTL improves model transferability and generalizability, increasing prediction accuracy and offering a resource‐efficient tool to accelerate selection for complex and labor‐intensive traits in modern breeding programs.

Plant phenotyping relevance

UAVマルチスペクトル画像から抽出した植物形質を用い、深層転移学習による穀粒収量予測の汎化性・転移性を検証しており、表現型取得・推定ワークフローが中心である。

Code and data availability

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