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.
Abstract (translated)
作物育种的目标之一是提高产量。通过预测不同育种材料的潜在产量,育种者可以在生长的不同阶段筛选这些材料,并选择表现最佳的品种。利用无人驾驶飞行器遥感技术收集育种区域内的高通量作物表型数据,为育种决策提供数据支持。然而,目前的产量预测准确性仍有待提高,且当前的产量预报工具在实用性和易用性方面仍存在不足。 为了应对这些挑战,本研究提出了一种基于混合方法和工具进行作物产量预测的方法。该方法允许育种者通过与大型语言模型(LLM)对话来交互式和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融入WOFOST模型中。然后,利用选定的同化过程输出以及遥感反演结果驱动时间序列时序融合变换器模型进行小麦产量预测。最后,在这种方法的基础上,并结合使用具有检索增强生成技术的大规模语言模型(LLM),我们开发了一个互动性高且支持可持续数据更新的小麦产量预测网络工具,该工具整合了多源数据以协助育种决策。 本研究旨在加速在育种过程中识别高产材料的进程,提高育种效率,并使育种决策更加科学和智能化。
URL
https://arxiv.org/abs/2501.04487