ABSTRACT

The prediction of fruit yield in the next generation is one of the most important breeding objectives in agricultural research. For this purpose, different generations of coriander consisted of six quietly divergent parents, their 15 F₁ hybrids and 15 F₂ families were evaluated during the 2014–2017 growing seasons. The artificial neural network (ANN) models were constructed to predict the fruit yield using morphological and agronomic factors, and compare the performance of ANN models with multiple linear regression (MLR) models. According to the principal component analysis (PCA) and stepwise regression (SWR), four traits of days to flowering, thousand fruit weight, fertile umbel number per plant and branch number per plant were selected as input variables in both ANN and MLR models. A network with Levenberg–Marquart learning algorithm, SigmoidAxon transfer function, one hidden layer with four neurons and having 0.461 root-mean-square error (RMSE), 0.335 mean absolute error (MAE) and 0.938 determination coefficient (R²) selected as the final ANN model. The ANN model was a more accurate tool rather than MLR for predicting fruit yield in coriander. According to sensitivity analysis, days to flowering and thousand fruit weight traits were identified as the most effective characters in fruit yield.

摘要

下一代果实产量的预测是农业研究中最重要的育种目标之一。为此目的，不同代的香菜由 6 个悄然不同的亲本、15 个 F ₁杂种和 15 个 F _2组成。在 2014-2017 年的生长季节对家庭进行了评估。构建人工神经网络（ANN）模型，利用形态和农艺因素预测果实产量，并将人工神经网络模型与多元线性回归（MLR）模型的性能进行比较。根据主成分分析（PCA）和逐步回归（SWR），在ANN和MLR模型中均选择开花天数、千果重、单株可育伞形花序数和单株分枝数4个性状作为输入变量。具有 Levenberg-Marquart 学习算法的网络，SigmoidAxon 传递函数，一个具有四个神经元的隐藏层，具有 0.461 均方根误差 (RMSE)、0.335 平均绝对误差 (MAE) 和 0.938 决定系数 (R ²) 被选为最终的 ANN 模型。ANN 模型是一种比 MLR 更准确的工具来预测香菜的果实产量。根据敏感性分析，开花天数和千果重性状被确定为影响果实产量的最有效性状。