论文标题
通过遥感和深度学习来估算作物产量
Estimating crop yields with remote sensing and deep learning
论文作者
论文摘要
提高农作物产量估计的准确性可以使整个作物生产链的改善，使农民可以更好地计划收获，并让保险公司更好地了解生产风险，并将其列为一些优势。为了执行他们的预测，大多数当前的机器学习模型都使用NDVI数据，这可能很难使用，这是由于云中的存在及其在获得的图像中的阴影，并且由于没有用于大面积的可靠农作物面具，尤其是在发展中国家。在本文中，我们提出了一个深度学习模型，能够对五种不同的农作物进行季前赛和季节预测。我们的模型使用农作物日历，易于获取的遥感数据和天气预报信息来提供准确的收益率估算。
Increasing the accuracy of crop yield estimates may allow improvements in the whole crop production chain, allowing farmers to better plan for harvest, and for insurers to better understand risks of production, to name a few advantages. To perform their predictions, most current machine learning models use NDVI data, which can be hard to use, due to the presence of clouds and their shadows in acquired images, and due to the absence of reliable crop masks for large areas, especially in developing countries. In this paper, we present a deep learning model able to perform pre-season and in-season predictions for five different crops. Our model uses crop calendars, easy-to-obtain remote sensing data and weather forecast information to provide accurate yield estimates.
