Integrating remote sensing data assimilation, deep learning and large language model for interactive wheat breeding yield prediction

Abstract

Yield is one of the core goals of crop breeding. By predicting the potential yield of different breeding materials, breeders can screen these materials at various growth stages to select the best performing. Based on unmanned aerial vehicle remote sensing technology, high-throughput crop phenotyping data in breeding areas is collected to provide data support for the breeding decisions of breeders. However, the accuracy of current yield predictions still requires improvement, and the usability and user-friendliness of yield forecasting tools remain suboptimal. To address these challenges, this study introduces a hybrid method and tool for crop yield prediction, designed to allow breeders to interactively and accurately predict wheat yield by chatting with a large language model (LLM). First, the newly designed data assimilation algorithm is used to assimilate the leaf area index into the WOFOST model. Then, selected outputs from the assimilation process, along with remote sensing inversion results, are used to drive the time-series temporal fusion transformer model for wheat yield prediction. Finally, based on this hybrid method and leveraging an LLM with retrieval augmented generation technology, we developed an interactive yield prediction Web tool that is user-friendly and supports sustainable data updates. This tool integrates multi-source data to assist breeding decision-making. This study aims to accelerate the identification of high-yield materials in the breeding process, enhance breeding efficiency, and enable more scientific and smart breeding decisions.

Plant phenotyping relevance

UAVリモートセンシングによる作物フェノタイピングデータを基盤に、収量推定手法と対話型Webツールを開発しており、植物形質（小麦収量）の取得・推定が研究の中心である。

Code and data availability

The article states that all study data (UAV remote sensing, LAI/CH phenotyping, yield, meteorological and soil data) are publicly available via a Zenodo deposit, which directly reproduces this paper's plant-phenotyping measurements. No author analysis code or trained model checkpoint URL is explicitly provided; other L