Estimating Soil Parameters from Hyperspectral Imagesusing Ensembles of Classic and Deep Machine Learning Models

Recent advances in remote sensing and artificial intelligence offer exciting opportunities in an array of fields, with precision agriculture being a notable use case. Here, estimating soil parameters from remotely-sensed hyperspectral imagery at a global scale can play a pivotal role in day-to-day operations, as it may help optimize agricultural management processes, hence positively affecting our planet. In this paper, we tackled the problem of estimating soil parameters from hyperspectral images and introduced heterogeneous regression ensembles for this task. They not only benefit from both classic and deep machine learning models but were also thoroughly investigated and fine-tuned in our rigorous experimental study, performed over a well-established HYPERVIEW benchmark dataset. The experiments showed that such heterogeneous ensembles outperform other techniques and offer a high level of model flexibility.