This study proposed a model that utilized machine learning algorithms to predict the yield of maize ( L.) cultivars. This will enable the selection of good cultivars with high yields that are suitable for planting in specific areas, such as a district or county. The breeding value of the cultivars and 11 types of time-series meteorological variables were selected as the input parameters of the model. The yield was set as the output parameter of the model. Random forest (RF), Levenberg–Marquardt neural network, and multilayer perceptron neural network algorithms were used to construct the model. The results showed that the RF outperformed the other algorithms in predicting the yield of maize cultivars by achieving the maximum coefficient of determination (R) of 0.77 and minimal root-mean-square error of 320.25 kg/acre, mean absolute error of 229.84 kg/acre, and mean absolute percentage error of 7.1%. The constructed model can be used to effectively predict the yield of specific varieties to enable the selection of good varieties in specific areas, such as a district or county. A prediction of the yield of a specific maize cultivar in a particular planting environment can have considerable value. It can facilitate the objective identification of better adapting cultivars among farmers and support the precise promotion and planting of cultivars.

中文翻译：

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基于机器学习算法预测玉米品种产量，精准推广种植


本研究提出了一种利用机器学习算法来预测玉米 (L.) 品种产量的模型。这将有助于选择适合在特定地区（例如区或县）种植的高产优质品种。选择品种育种值和11类时间序列气象变量作为模型的输入参数。将产量设置为模型的输出参数。使用随机森林（RF）、Levenberg-Marquardt 神经网络和多层感知器神经网络算法来构建模型。结果表明，RF 在预测玉米品种产量方面优于其他算法，最大决定系数 (R) 为 0.77，最小均方根误差为 320.25 kg/英亩，平均绝对误差为 229.84 kg/英亩。英亩，平均绝对百分比误差为 7.1%。所构建的模型可用于有效预测特定品种的产量，从而能够在特定区域（例如区县）选择优良品种。预测特定玉米品种在特定种植环境中的产量具有相当大的价值。有助于农民客观鉴定适应性较好的品种，支持品种的精准推广和种植。