论文标题
使用深度转移学习从遥感数据中同时从遥感数据中预测玉米和大豆产量
Simultaneous Corn and Soybean Yield Prediction from Remote Sensing Data Using Deep Transfer Learning
论文作者
论文摘要
大规模的农作物产量估计部分是由于遥感数据的可用性，因此可以在整个生长周期中持续监测农作物。拥有此信息使利益相关者能够做出实时决策以最大程度地发挥收益潜力。尽管存在从遥感数据中预测产量的各种模型，但目前不存在一种可以同时估算多种农作物产量的方法，从而导致更准确的预测。一种预测多种农作物产量并同时考虑多种作物产量之间相互作用的模型。我们提出了一个名为FarrefNet的新的卷积神经网络模型，该模型利用了一个新颖的深度学习框架，该框架通过共享骨干特征提取器的权重，利用玉米和大豆产量预测之间的转移学习。此外，要考虑多目标响应变量，我们提出了一个新的损失函数。我们使用来自美国大豆的1,132个县和1,076个县的1,132个县的数据进行实验。数值结果表明，我们所提出的方法在收获前的玉米和大豆产量准确地预测了玉米和大豆的产量，MAE分别为平均收益率的8.74％和8.70％，并且与其他最新方法具有竞争力。
Large-scale crop yield estimation is, in part, made possible due to the availability of remote sensing data allowing for the continuous monitoring of crops throughout their growth cycle. Having this information allows stakeholders the ability to make real-time decisions to maximize yield potential. Although various models exist that predict yield from remote sensing data, there currently does not exist an approach that can estimate yield for multiple crops simultaneously, and thus leads to more accurate predictions. A model that predicts the yield of multiple crops and concurrently considers the interaction between multiple crop yields. We propose a new convolutional neural network model called YieldNet which utilizes a novel deep learning framework that uses transfer learning between corn and soybean yield predictions by sharing the weights of the backbone feature extractor. Additionally, to consider the multi-target response variable, we propose a new loss function. We conduct our experiment using data from 1,132 counties for corn and 1,076 counties for soybean across the United States. Numerical results demonstrate that our proposed method accurately predicts corn and soybean yield from one to four months before the harvest with a MAE being 8.74% and 8.70% of the average yield, respectively, and is competitive to other state-of-the-art approaches.
