Crop growth modeling and yield forecasting are essential to improve food security policies worldwide. To estimate potato (Solanum tubersum L.) yield over Mexico at a municipal level, we used meteorological data provided by the ERA5 (ECMWF Re-Analysis) dataset developed by the Copernicus Climate Change Service, satellite imagery from the TERRA platform, and field information. Five different machine learning algorithms were used to build the models: random forest (rf), support vector machine linear (svmL), support vector machine polynomial (svmP), support vector machine radial (svmR), and general linear model (glm). The optimized models were tested using independent data (2017 and 2018) not used in the training and optimization phase (2004–2016). In terms of percent root mean squared error (%RMSE), the best results were obtained by the rf algorithm in the winter cycle using variables from the first three months of the cycle (R² = 0.757 and %RMSE = 18.9). For the summer cycle, the best performing model was the svmP which used the first five months of the cycle as variables (R² = 0.858 and %RMSE = 14.9). Our results indicated that adding predictor variables of the last two months before the harvest did not significantly improved model performances. These results demonstrate that our models can predict potato yield by analyzing the yield of the previous year, the general conditions of NDVI, meteorology, and information related to the irrigation system at a municipal level.

中文翻译：

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使用市级卫星数据估算马铃薯产量：一种机器学习方法

作物生长模型和产量预测对于改善全球粮食安全政策至关重要。估计马铃薯（Solanum tubersum L.）的产量，我们使用了哥白尼气候变化服务局开发的ERA5（ECMWF再分析）数据集提供的气象数据，TERRA平台的卫星图像以及现场信息。使用五种不同的机器学习算法来构建模型：随机森林（rf），支持向量机线性（svmL），支持向量机多项式（svmP），支持向量机径向（svmR）和通用线性模型（glm）。使用训练和优化阶段（2004–2016）未使用的独立数据（2017和2018）对优化模型进行了测试。就均方根误差百分比（％RMSE）而言，rf算法在冬季周期中使用周期前三个月的变量（R ²获得了最佳结果）= 0.757，％RMSE = 18.9）。对于夏季周期，性能最佳的模型是svmP，该模型使用周期的前五个月作为变量（R ² = 0.858和％RMSE = 14.9）。我们的结果表明，在收获前的最后两个月添加预测变量并不能显着改善模型性能。这些结果表明，我们的模型可以通过分析上一年的产量，NDVI的一般状况，气象学以及与市级灌溉系统有关的信息来预测马铃薯的产量。