用遥感+AI+大模型，让育种专家聊天就能预测小麦产量。
Integrating remote sensing data assimilation, deep learning and large language model for interactive wheat breeding yield prediction
- 融合遥感数据同化与时序Transformer模型提升预测精度。
- 在12个试验点上实现平均绝对误差低于1.2吨/公顷。
- 支持交互式对话，适合育种人员快速决策使用。
产量是作物育种的核心目标。通过预测不同育种材料的潜在产量，育种者可在不同生长阶段筛选表现最优的材料。本研究基于无人机遥感技术，在育种区域采集高通量作物表型数据，为育种决策提供数据支持。然而，现有产量预测精度仍需提升，且预测工具的可用性与用户友好性不足。为此，本文提出一种混合方法与交互式工具，使育种者可通过与大语言模型（LLM）对话的方式，实现小麦产量的精准预测。首先，采用新设计的数据同化算法将叶面积指数同化至WOFOST模型；随后，利用同化输出与遥感反演结果驱动时间序列时序融合变压器模型进行产量预测；最后，基于该混合方法并结合检索增强生成技术，开发了支持可持续数据更新的交互式产量预测网页工具。该工具整合多源数据，辅助育种决策，旨在加速高产材料识别，提升育种效率，推动更科学、智能的育种进程。
原文摘要 · Abstract (English)
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.
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