Accurate prediction of crop yield before harvest is of great importance for crop logistics, market planning, and food distribution around the world. Yield prediction requires monitoring of phenological and climatic characteristics over extended time periods to model the complex relations involved in crop development. Remote sensing satellite images provided by various satellites circumnavigating the world are a cheap and reliable way to obtain data for yield prediction. The field of yield prediction is currently dominated by Deep Learning approaches. While the accuracies reached with those approaches are promising, the needed amounts of data and the ``black-box'' nature can restrict the application of Deep Learning methods. The limitations can be overcome by proposing a pipeline to process remote sensing images into feature-based representations that allow the employment of Extreme Gradient Boosting (XGBoost) for yield prediction. A comparative evaluation of soybean yield prediction within the United States shows promising prediction accuracies compared to state-of-the-art yield prediction systems based on Deep Learning. Feature importances expose the near-infrared spectrum of light as an important feature within our models. The reported results hint at the capabilities of XGBoost for yield prediction and encourage future experiments with XGBoost for yield prediction on other crops in regions all around the world.
翻译:对收获前作物产量的准确预测对作物物流、市场规划和粮食分布极为重要。对产量的预测需要长期监测动物和气候特性,以模拟作物开发所涉及的复杂关系。环绕世界的各卫星提供的遥感卫星图像是获取产量预测数据的一种廉价和可靠的方法。产量预测领域目前以深学习方法为主。虽然这些方法所达到的准确度很有希望,但所需的数据数量和“黑盒子”的性质可以限制深层学习方法的应用。通过提出一条管道,将遥感图像处理成基于地貌的图象,以便能够利用极端梯级推进器(XGBoost)进行产量预测,可以克服这些局限性。对美国范围内的豆类产量预测进行比较评价,表明与以深学习为基础的最新产值预测系统相比,预测的准确性很有希望。具体重要性将所有近红外光谱暴露为我们模型中的一个重要特征。所报告的结果提示是XGB区域未来产量预测的预测能力,鼓励XGB区域其他作物产量预测。