DeepYield: A combined convolutional neural network with long short-term memory for crop yield forecasting
DeepYield: A combined convolutional neural network with long short-term memory for crop yield forecasting
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DeepYield：具有长短期记忆的组合卷积神经网络用于作物产量预测
DOI：
10.1016/j.eswa.2021.115511
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发表时间：
2021-07-08
影响因子：
8.5
通讯作者：
Moradkhani, Hamid
中科院分区：
文献类型：
--
作者：
Gavahi, Keyhan;Abbaszadeh, Peyman;Moradkhani, Hamid
作物产量预测对作物市场规划、作物保险、收获管理和养分优化管理具有重要意义。常用的作物预测方法包括但不限于进行广泛的人工调查或使用遥感数据。考虑到遥感影像提供的数据量越来越大，这种方法在作物产量预测任务中变得越来越重要，需要更复杂的方法来提取这些数据的内在时空模式。尽管使用卷积神经网络（CNN）等深度学习（DL）方法在这一领域取得了相当大的进展，但之前没有研究调查使用卷积长短期记忆（ConvLSTM）进行作物产量预测。在这里，我们提出了一种组合结构DeepYield，它将ConvLSTM层与三维CNN （3DCNN）相结合，以获得更准确、更可靠的时空特征提取。这些模型使用基于县的历史产量数据和MODIS地表温度（LST）、地表反射率（SR）和土地覆盖（LC）数据进行训练，这些数据来自美国本土1836个主要大豆种植县。将所开发模型的预测性能与包括决策树、CNN + GP和CNNLSTM在内的竞争方法进行了比较，结果表明DeepYield显著优于这些技术，并且优于ConvLSTM和3DCNN。
Crop yield forecasting is of great importance to crop market planning, crop insurance, harvest management, and optimal nutrient management. Commonly used approaches for crop prediction include but are not limited to conducting extensive manual surveys or using data from remote sensing. Considering the increasing amount of data provided by remote sensing imagery, this approach is becoming increasingly important for the task of crop yield forecasting and there is a need for more sophisticated approaches to extract the inherent spatiotemporal patterns of these data. Although considerable progress has been made in this field by using Deep Learning (DL) methods such as Convolutional Neural Networks (CNN), no study before has investigated the use of Convolutional Long Short-Term Memory (ConvLSTM) for crop yield forecasting. Here, we propose DeepYield, a combined structure, that integrates the ConvLSTM layers with the 3-Dimensional CNN (3DCNN) for more accurate and reliable spatiotemporal feature extraction. The models are trained by using county-based historical yield data and MODIS Land Surface Temperature (LST), Surface Reflectance (SR), and Land Cover (LC) data over 1836 primary soybean growing counites in the Contiguous United States (CONUS). The forecasting performance of the developed models is compared against the competing approaches including Decision Trees, CNN + GP, and CNNLSTM and results indicate that DeepYield significantly outperforms these techniques and also performs better than both ConvLSTM and 3DCNN.