Pattern Based Multivariable Regression using Deep Learning (PBMR-DP)
Computer Vision and Pattern Recognition
2022-03-11 v3 Artificial Intelligence
Machine Learning
Image and Video Processing
Abstract
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.
Cite
@article{arxiv.2202.13541,
title = {Pattern Based Multivariable Regression using Deep Learning (PBMR-DP)},
author = {Jiztom Kavalakkatt Francis and Chandan Kumar and Jansel Herrera-Gerena and Kundan Kumar and Matthew J Darr},
journal= {arXiv preprint arXiv:2202.13541},
year = {2022}
}
Comments
7 pages, 5 figures, 3 tables