论文标题
小麦作物的产量预测使用深度LSTM模型
Wheat Crop Yield Prediction Using Deep LSTM Model
论文作者
论文摘要
收获之前的季节性早期作物预测可以使农民受益，以改善生产，并使各种机构能够相应地制定计划。我们引入了一种可靠且廉价的方法，可以预测公开可用的卫星图像的作物产量。所提出的方法直接在原始卫星图像上起作用，而无需提取任何手工制作的特征或在图像上执行尺寸降低。该方法隐式地模拟了生长季节不同步骤的相关性以及卫星图像中的各个乐队。我们评估了印度几个州的TEHSIL（块）水平小麦预测的拟议方法，并表明它的表现优于50 \％以上的现有方法。我们还表明，结合其他上下文信息，例如农田，水域和城市地区的位置有助于改善收益率的估计。
An in-season early crop yield forecast before harvest can benefit the farmers to improve the production and enable various agencies to devise plans accordingly. We introduce a reliable and inexpensive method to predict crop yields from publicly available satellite imagery. The proposed method works directly on raw satellite imagery without the need to extract any hand-crafted features or perform dimensionality reduction on the images. The approach implicitly models the relevance of the different steps in the growing season and the various bands in the satellite imagery. We evaluate the proposed approach on tehsil (block) level wheat predictions across several states in India and demonstrate that it outperforms existing methods by over 50\%. We also show that incorporating additional contextual information such as the location of farmlands, water bodies, and urban areas helps in improving the yield estimates.
