利用遥感和深度学习估算作物产量
Estimating crop yields with remote sensing and deep learning
摘要：
提高作物产量估计的准确性可能会改善整个作物生产链，使农民能够更好地规划收获，保险公司能够更好地了解生产风险，仅举几个优势。为了进行预测，大多数当前的机器学习模型使用NDVI数据，由于所获取的图像中存在云及其阴影，并且由于缺乏可靠的大面积作物掩模，特别是在发展中国家，因此很难使用。在本文中，我们提出了一个深度学习模型，能够对五种不同的作物进行季节前和季节预测。我们的模型使用作物日历，易于获得的遥感数据和天气预报信息，以提供准确的产量估计。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
