论文标题
使用深度学习（PBMR-DP）的基于模式的多变量回归
Pattern Based Multivariable Regression using Deep Learning (PBMR-DP)
论文作者
论文摘要
我们为多元回归提出了一种深度学习方法，该方法基于模式识别，该模式识别触发了对传感器数据的快速学习。我们使用了传感器到图像的转换，使我们能够利用计算机视觉架构和培训过程。除了这些数据准备方法外，我们还探索了最先进的体系结构来生成回归输出以预测农业作物连续产量信息。最后，我们与MLCAS2021中报道的一些顶级模型进行了比较。我们发现，使用直接的训练过程，我们能够完成4.394，RMSE为5.945的MAE和0.861的R^2。
We propose a deep learning methodology for multivariate regression that is based on pattern recognition that triggers fast learning over sensor data. We used a conversion of sensors-to-image which enables us to take advantage of Computer Vision architectures and training processes. In addition to this data preparation methodology, we explore the use of state-of-the-art architectures to generate regression outputs to predict agricultural crop continuous yield information. Finally, we compare with some of the top models reported in MLCAS2021. We found that using a straightforward training process, we were able to accomplish an MAE of 4.394, RMSE of 5.945, and R^2 of 0.861.
