Carneiro V Q, Silva G N, Cruz C D, Carneiro P C S, Nascimento M, Carneiro J E S
Departamento de Biologia Geral, Universidade Federal de Viçosa, Viçosa, MG, Brasil
Laboratório de Bioinformática (BIOAGRO), Viçosa, MG, Brasil
Genet Mol Res. 2017 Jun 29;16(2):gmr-16-02-gmr.16029500. doi: 10.4238/gmr16029500.
Classification using a scale of visual notes is a strategy used to select erect bean plants in order to improve bean plant architectures. Use of morphological traits associated with the phenotypic expression of bean architecture in classification procedures may enhance selection. The objective of this study was to evaluate the potential of artificial neural networks (ANNs) as auxiliary tools in the improvement of bean plant architecture. Data from 19 lines were evaluated for 22 traits, in 2007 and 2009 winter crops. Hypocotyl diameter and plant height were selected for analysis through ANNs. For classification purposes, these lines were separated into two groups, determined by the plant architecture notes. The predictive ability of ANNs was evaluated according to two scenarios to predict the plant architecture - training with 2007 data and validating in 2009 data (scenario 1), and vice versa (scenario 2). For this, ANNs were trained and validated using data from replicates of the evaluated lines for hypocotyl diameter individually, or together with the mean height of plants in the plot. In each scenario, the use of data from replicates or line means was evaluated for prediction through previously trained and validated ANNs. In both scenarios, ANNs based on hypocotyl diameter and mean height of plants were superior, since the error rates obtained were lower than those obtained using hypocotyl diameter only. Lower apparent error rates were verified in both scenarios for prediction when data on the means of the evaluated traits were submitted to better trained and validated ANNs.
使用视觉评分量表进行分类是一种用于选择直立型菜豆植株以改善菜豆植株结构的策略。在分类过程中使用与菜豆结构表型表达相关的形态性状可能会增强选择效果。本研究的目的是评估人工神经网络（ANN）作为辅助工具在改善菜豆植株结构方面的潜力。在2007年和2009年冬季作物中，对19个品系的22个性状数据进行了评估。通过人工神经网络选择下胚轴直径和株高进行分析。为了进行分类，根据植株结构评分将这些品系分为两组。根据两种预测植株结构的情景评估人工神经网络的预测能力——用2007年的数据进行训练并在2009年的数据中进行验证（情景1），反之亦然（情景2）。为此，使用评估品系下胚轴直径重复数据单独地，或与小区内植株平均高度一起，对人工神经网络进行训练和验证。在每种情景下，通过先前训练和验证的人工神经网络评估使用重复数据或品系均值数据进行预测的情况。在两种情景中，基于下胚轴直径和植株平均高度的人工神经网络表现更优，因为获得的错误率低于仅使用下胚轴直径时获得的错误率。当将评估性状的均值数据提交给训练和验证更好的人工神经网络时，在两种情景下预测都验证了较低的表观错误率。
