利用植被指数和计算智能对玉米和大豆基因型进行高通量表型分析。

IF 4.7 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Paulo E Teodoro, Larissa P R Teodoro, Fabio H R Baio, Carlos A Silva Junior, Dthenifer C Santana, Leonardo L Bhering
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引用次数: 0

摘要

建立可对全球经济利益相关作物的复杂农艺性状(如大豆和玉米的谷物产量)进行表型评估的模型,对于提高育种计划的效率至关重要。从这个意义上说,了解农艺学变量与高通量表型(HTP)所获变量之间的关系对实现这一目标至关重要。我们的假设是,通过高通量表型获得的植被指数(VIs)可用于间接测量一年生作物的农艺变量。我们的目标是研究玉米和大豆基因型中的农艺变量与遥感获得的植被指数之间的关联,并根据作为模型输入的植被指数,确定预测这些作物 GY 的计算智能。在 2020/2021、2021/2022 和 2022/2023 作物季节对 30 种玉米基因型进行了比较试验,在 2021/2022 和 2022/2023 作物季节对 32 种大豆基因型进行了比较试验。在所有试验中,都在 R1 阶段使用配备了多光谱传感器的无人机 Sensefly eBee 进行了飞越飞行,以获取绿色(550 nm)、红色(660 nm)、近红外(735 nm)和红外(790 nm)波长的冠层反射率,用于计算所评估的 VIs。对玉米作物农艺性状的评估包括:叶氮含量、株高、第一穗插入高度和生长期;对大豆农艺性状的评估包括:成熟天数、株高、第一荚插入高度和生长期。变量之间的关联通过相关网络来表示,为了确定哪些指数与所评估的每个性状最相关,还进行了路径分析。最后,在多元回归模型(MLR)和人工神经网络(ANN)中采用了玉米和大豆试验中与各变量有因果关系的VIs作为独立解释变量。我们的研究结果表明,植被指数可用于预测玉米和大豆的农艺变量。土壤调整植被指数(SAVI)和绿色归一化差异植被指数(GNDVI)对玉米的所有农艺变量都有很高的直接正向影响,而归一化差异植被指数(NDVI)和归一化差异红边指数(NDRE)与大豆的所有变量都有正向因果关系。ANN 的表现优于 MLR,在使用路径分析选择的植被指数作为输入预测农艺变量时具有更高的准确性。未来的研究应评估其他植物性状,如生理或营养性状,以及与本文评估不同的光谱变量,以期有助于深入了解植物性状与光谱变量之间的因果关系。此类研究有助于在每种作物的相关性状水平上实现更具体的 HTP,从而帮助开发出满足未来人口增长和气候变化需求的遗传材料。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High-throughput phenotyping in maize and soybean genotypes using vegetation indices and computational intelligence.

Building models that allow phenotypic evaluation of complex agronomic traits in crops of global economic interest, such as grain yield (GY) in soybean and maize, is essential for improving the efficiency of breeding programs. In this sense, understanding the relationships between agronomic variables and those obtained by high-throughput phenotyping (HTP) is crucial to this goal. Our hypothesis is that vegetation indices (VIs) obtained from HTP can be used to indirectly measure agronomic variables in annual crops. The objectives were to study the association between agronomic variables in maize and soybean genotypes with VIs obtained from remote sensing and to identify computational intelligence for predicting GY of these crops from VIs as input in the models. Comparative trials were carried out with 30 maize genotypes in the 2020/2021, 2021/2022 and 2022/2023 crop seasons, and with 32 soybean genotypes in the 2021/2022 and 2022/2023 seasons. In all trials, an overflight was performed at R1 stage using the UAV Sensefly eBee equipped with a multispectral sensor for acquiring canopy reflectance in the green (550 nm), red (660 nm), near-infrared (735 nm) and infrared (790 nm) wavelengths, which were used to calculate the VIs assessed. Agronomic traits evaluated in maize crop were: leaf nitrogen content, plant height, first ear insertion height, and GY, while agronomic traits evaluated in soybean were: days to maturity, plant height, first pod insertion height, and GY. The association between the variables were expressed by a correlation network, and to identify which indices are best associated with each of the traits evaluated, a path analysis was performed. Lastly, VIs with a cause-and-effect association on each variable in maize and soybean trials were adopted as independent explanatory variables in multiple regression model (MLR) and artificial neural network (ANN), in which the 10 best topologies able to simultaneously predict all the agronomic variables evaluated in each crop were selected. Our findings reveal that VIs can be used to predict agronomic variables in maize and soybean. Soil-adjusted Vegetation Index (SAVI) and Green Normalized Dif-ference Vegetation Index (GNDVI) have a positive and high direct effect on all agronomic variables evaluated in maize, while Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) have a positive cause-and-effect association with all soybean variables. ANN outperformed MLR, providing higher accuracy when predicting agronomic variables using the VIs select by path analysis as input. Future studies should evaluate other plant traits, such as physiological or nutritional ones, as well as different spectral variables from those evaluated here, with a view to contributing to an in-depth understanding about cause-and-effect relationships between plant traits and spectral variables. Such studies could contribute to more specific HTP at the level of traits of interest in each crop, helping to develop genetic materials that meet the future demands of population growth and climate change.

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来源期刊
Plant Methods
Plant Methods 生物-植物科学
CiteScore
9.20
自引率
3.90%
发文量
121
审稿时长
2 months
期刊介绍: Plant Methods is an open access, peer-reviewed, online journal for the plant research community that encompasses all aspects of technological innovation in the plant sciences. There is no doubt that we have entered an exciting new era in plant biology. The completion of the Arabidopsis genome sequence, and the rapid progress being made in other plant genomics projects are providing unparalleled opportunities for progress in all areas of plant science. Nevertheless, enormous challenges lie ahead if we are to understand the function of every gene in the genome, and how the individual parts work together to make the whole organism. Achieving these goals will require an unprecedented collaborative effort, combining high-throughput, system-wide technologies with more focused approaches that integrate traditional disciplines such as cell biology, biochemistry and molecular genetics. Technological innovation is probably the most important catalyst for progress in any scientific discipline. Plant Methods’ goal is to stimulate the development and adoption of new and improved techniques and research tools and, where appropriate, to promote consistency of methodologies for better integration of data from different laboratories.
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