Accurate and timely information on yield forecasting is necessary for policymakers in decision-making. The case study was planned to develop a framework for the regional wheat yield forecasting model for southeastern Turkey. Therefore, after implementing Top of Atmospheric (TOA) correction for all images and collecting ground-truthing point of 313 fields from the Nurdagi and Islahiye counties. A total of eight machine learning algorithms were tuned and tested for the satellite image classification so that best model was used for the spatial distribution of wheat crop. The results of machine learning algorithms showed an accuracy greater than 90%. As the best model, the random forest was used for image classification. The classification results showed that area estimated by the classifier were 11% more than those reported by the Turkish statistical department. The observed and predicted yield of the tested model was closed to each other with root mean square error (RMSE) of 198 kg ha−1. The observed and predicted yield showed a close agreement with RMSE of 144 kg ha−1 at Nurdagi and 68 kg ha−1 at Islahiye for 5 years. It is concluded that remote sensing is useful tools for estimation of yield and developed can be used for other regions and crops.

中文翻译：

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使用机器学习方法估算土耳其东南部小麦的面积和产量

决策者需要准确及时的产量预测信息。该案例研究旨在为土耳其东南部的区域小麦产量预测模型开发一个框架。因此，在对所有图像实施大气顶（TOA）校正并收集来自 Nurdagi 和 Islahiye 县的 313 个字段的地面真实点后。总共对八种机器学习算法进行了卫星图像分类调整和测试，以便将最佳模型用于小麦作物的空间分布。机器学习算法的结果显示准确率超过 90%。作为最好的模型，随机森林被用于图像分类。分类结果显示，分类器估计的面积比土耳其统计部门报告的面积多 11%。测试模型的观察和预测产量彼此接近，均方根误差 (RMSE) 为 198 kg ha-1。观察到的和预测的产量与 5 年内 Nurdagi 的 144 kg ha-1 和 Islahiye 的 68 kg ha-1 的 RMSE 非常一致。得出的结论是，遥感是估算产量的有用工具，并且已开发可用于其他地区和作物。