GSP-AI: An AI-Powered Platform for Identifying Key Growth Stages and the Vegetative-to-Reproductive Transition in Wheat Using Trilateral Drone Imagery and Meteorological Data.

IF 7.6 1区 农林科学 Q1 AGRONOMY Plant Phenomics Pub Date : 2024-10-09 eCollection Date: 2024-01-01 DOI:10.34133/plantphenomics.0255
Liyan Shen, Guohui Ding, Robert Jackson, Mujahid Ali, Shuchen Liu, Arthur Mitchell, Yeyin Shi, Xuqi Lu, Jie Dai, Greg Deakin, Katherine Frels, Haiyan Cen, Yu-Feng Ge, Ji Zhou
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Abstract

Wheat (Triticum aestivum) is one of the most important staple crops worldwide. To ensure its global supply, the timing and duration of its growth cycle needs to be closely monitored in the field so that necessary crop management activities can be arranged in a timely manner. Also, breeders and plant researchers need to evaluate growth stages (GSs) for tens of thousands of genotypes at the plot level, at different sites and across multiple seasons. These indicate the importance of providing a reliable and scalable toolkit to address the challenge so that the plot-level assessment of GS can be successfully conducted for different objectives in plant research. Here, we present a multimodal deep learning model called GSP-AI, capable of identifying key GSs and predicting the vegetative-to-reproductive transition (i.e., flowering days) in wheat based on drone-collected canopy images and multiseasonal climatic datasets. In the study, we first established an open Wheat Growth Stage Prediction (WGSP) dataset, consisting of 70,410 annotated images collected from 54 varieties cultivated in China, 109 in the United Kingdom, and 100 in the United States together with key climatic factors. Then, we built an effective learning architecture based on Res2Net and long short-term memory (LSTM) to learn canopy-level vision features and patterns of climatic changes between 2018 and 2021 growing seasons. Utilizing the model, we achieved an overall accuracy of 91.2% in identifying key GS and an average root mean square error (RMSE) of 5.6 d for forecasting the flowering days compared with manual scoring. We further tested and improved the GSP-AI model with high-resolution smartphone images collected in the 2021/2022 season in China, through which the accuracy of the model was enhanced to 93.4% for GS and RMSE reduced to 4.7 d for the flowering prediction. As a result, we believe that our work demonstrates a valuable advance to inform breeders and growers regarding the timing and duration of key plant growth and development phases at the plot level, facilitating them to conduct more effective crop selection and make agronomic decisions under complicated field conditions for wheat improvement.

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GSP-AI:利用三边无人机图像和气象数据识别小麦关键生长阶段和无性到生殖过渡的人工智能平台。
小麦(Triticum aestivum)是全球最重要的主粮作物之一。为确保其全球供应,需要在田间密切监测其生长周期的时间和持续时间,以便及时安排必要的作物管理活动。此外,育种人员和植物研究人员还需要在不同地点和多个季节对数以万计的基因型进行小区级生长阶段(GSs)评估。这些都表明,提供一个可靠且可扩展的工具包来应对这一挑战非常重要,这样就能成功地针对植物研究的不同目标进行小区级 GS 评估。在此,我们提出了一种名为 GSP-AI 的多模态深度学习模型,该模型能够基于无人机采集的冠层图像和多季节气候数据集,识别关键的 GSs 并预测小麦的无性到生殖过渡(即开花天数)。在这项研究中,我们首先建立了一个开放的小麦生长阶段预测(WGSP)数据集,该数据集包括从中国种植的54个品种、英国种植的109个品种和美国种植的100个品种中收集的70,410张注释图像以及关键气候因子。然后,我们建立了一个基于 Res2Net 和长短期记忆(LSTM)的有效学习架构,以学习冠层视觉特征和 2018 年至 2021 年生长季的气候变化规律。利用该模型,我们在识别关键GS方面取得了91.2%的总体准确率,与人工评分相比,花期预测的平均均方根误差(RMSE)为5.6 d。我们利用在中国 2021/2022 年采集的高分辨率智能手机图像进一步测试和改进了 GSP-AI 模型,通过该模型,GS 的准确率提高到 93.4%,花期预测的均方根误差降低到 4.7 d。因此,我们相信,我们的工作展示了一项宝贵的进步,为育种者和种植者提供了有关地块层面植物生长发育关键阶段的时间和持续时间的信息,有助于他们在复杂的田间条件下进行更有效的作物选择和农艺决策,从而改良小麦。
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来源期刊
Plant Phenomics
Plant Phenomics Multiple-
CiteScore
8.60
自引率
9.20%
发文量
26
审稿时长
14 weeks
期刊介绍: Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics. The scope of the journal covers the latest technologies in plant phenotyping for data acquisition, data management, data interpretation, modeling, and their practical applications for crop cultivation, plant breeding, forestry, horticulture, ecology, and other plant-related domains.
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