Explain it to me! On the use of explainable machine learning for the agricultural and environmental sciences
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Machine learning approaches, especially deep neural networks, are showing tremendous success in finding patterns and relationships in large data sets for predictions and classifications that are usually too complex to be directly captured by humans. In addition to high accuracy, a desired goal is to learn explainable models and understand how a particular decision was made. To achieve this goal and obtain explanations, knowledge from the domain is needed that can be integrated into the model or applied post-hoc. This talk presents diverse environmental and agricultural sciences applications in which explainable machine learning is used. It will also show that machine learning can not only be used to learn models that align with our existing knowledge but can also lead to new scientific insights.