Abstract: We propose a deep learning methodology for multivariable regression 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 using state-of-the-art architectures to generate regression outputs to predict agricultural crop continuous yield information. Finally, we compare with some 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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.861.
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