  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.
