集成遥感数据同化、深度学习与大语言模型用于交互式小麦育种产量预测
Integrating remote sensing data assimilation, deep learning and large language model for interactive wheat breeding yield prediction
AI总结：
针对小麦育种产量预测精度与工具易用性不足，提出融合遥感数据同化、时序深度学习模型及 RAG 增强 LLM 的交互式 Web 预测工具。
摘要：
产量是作物育种的核心目标之一。通过预测不同育种材料的潜在产量，育种者可以在各个生长阶段筛选这些材料，以选择表现最佳的材料。基于无人机遥感技术，研究收集了育种区的高通量作物表型数据，为育种者的育种决策提供数据支持。然而，当前产量预测的准确性仍有待提高，且产量预测工具的可用性和用户友好性仍不够理想。为应对这些挑战，本研究提出了一种用于作物产量预测的混合方法和工具，旨在使育种者能够通过与大语言模型（LLM）聊天，交互式且准确地预测小麦产量。首先，采用新设计的数据同化算法将叶面积指数同化到 WOFOST 模型中。随后，利用同化过程的选定输出以及遥感反演结果，驱动用于小麦产量预测的时序时间融合变换器模型。最后，基于该混合方法，并利用具备检索增强生成技术的 LLM，开发了一个用户友好、支持可持续数据更新的交互式产量预测 Web 工具。该工具集成多源数据以辅助育种决策。本研究旨在加快育种过程中高产材料的识别，提高育种效率，并实现更科学、智能的育种决策。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
