Root rot diseases of sugar beet resulted by Rhizoctonia solani, Pythium aphanidermatum can arouse significant losses in sugar beet crops. Simple and accurate statistical techniques are essential to predict sugar beet yield loss as an industrial crop for agricultural management specialists. The objectives of the present study were to compare: (1) the prediction capabilities of different statistical models at several regions and cultivars, (2) the effectiveness of artificial neural network (ANN) models to predict root rot yield loss, and (3) the effectiveness of simple and multiple linear regression models compare to ANN models. In order to measure crop loss, during two consecutive growing seasons, eleven different sugar beet cultivars were planted in the infected fields in Qazvin, Torbate Jam, Urmia and Kermanshah regions in Iran. To calculate the crop loss, the correlation between Disease incidence (DI) and the important components was considered. The highest correlation (P ≤ 0.01) was observed between DI and Root yield (RY), so we developed different yield loss models. Consequently, the best-fitted simple regression model obtained for RC01 and Urmia with 94.51 % and 90.44 % R-squared, respectively. Then the proper parameters specified in the regression models considered to develop an ANN model using Matlab software to predict the yield loss. Interestingly, obtained ANN models resulted in R², RMSE of 87.07 %, 5.01 and the lowest AIC vs. 86.78 % and 6.033 for final linear regression, respectively. Our results suggest that, based on the simplicity of the model, simple linear regression is the best model although the artificial neural network models are more accurate for prediction.

玉米丝核菌、Pythium aphanidermatum引起的甜菜根腐病会造成甜菜作物的重大损失。简单而准确的统计技术对于预测作为农业管理专家的经济作物的甜菜产量损失至关重要。本研究的目的是比较：(1) 多个地区和品种的不同统计模型的预测能力，(2) 人工神经网络 (ANN) 模型预测根腐病产量损失的有效性，以及 (3)与 ANN 模型相比，简单和多元线性回归模型的有效性。为了测量作物损失，在连续两个生长季节，在伊朗 Qazvin、Torbate Jam、Urmia 和 Kermanshah 地区的受感染田里种植了 11 个不同的甜菜品种。为了计算作物损失，考虑了疾病发生率 (DI) 与重要组成部分之间的相关性。在 DI 和根产量 (RY) 之间观察到最高的相关性 (P ≤ 0.01)，因此我们开发了不同的产量损失模型。因此，为 RC01 和 Urmia 获得的最佳拟合简单回归模型分别为 94.51% 和 90.44% R 平方。然后在回归模型中指定的适当参数考虑使用 Matlab 软件开发 ANN 模型来预测产量损失。有趣的是，获得的 ANN 模型导致 R 然后在回归模型中指定的适当参数考虑使用 Matlab 软件开发 ANN 模型来预测产量损失。有趣的是，获得的 ANN 模型导致 R 然后在回归模型中指定的适当参数考虑使用 Matlab 软件开发 ANN 模型来预测产量损失。有趣的是，获得的 ANN 模型导致 R^{如图 2 所示}，最终线性回归的 RMSE 分别为 87.07%、5.01 和最低 AIC，而 AIC 分别为 86.78% 和 6.033。我们的结果表明，基于模型的简单性，简单线性回归是最好的模型，尽管人工神经网络模型的预测更准确。