Athanasiadis N Ioannis

Abstract Predictor inputs and label data for crop yield forecasting are not always available at the same spatial resolution. We propose a deep learning framework that uses high resolution inputs (e.g. weather and soil) and low resolution labels (e.g. yield and crop area statistics) to produce crop yield forecasts for both spatial levels. The forecasting model is calibrated by weak supervision from low resolution crop area and yield statistics.

Home > Engineering > Civil and Environmental Engineering > iEMSs Conference > 2022 > STREAM-E > 16 Keywords representation learning; nitrogen response rate prediction; operational decision support Abstract Learning latent representations has aided operational decision-making in several disciplines. Its advantages include uncovering hidden interactions in data and automating procedures which were performed manually in the past.