Pattern Based Multivariable Regression using Deep Learning (PBMR-DP)
This work addresses crop yield prediction for agricultural applications, but it is incremental as it applies existing methods to a new data type.
The paper tackles multivariate regression for predicting agricultural crop yield by converting sensor data to images and using computer vision architectures, achieving an MAE of 4.394, RMSE of 5.945, and R^2 of 0.861.
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.