Balancing machine learning and artificial intelligence in soil science with human perspective and experience

Abstract

Machine learning and artificial intelligence continue to evolve at a rapid pace, with many potential applications related to soil science. Even so, human experience and perception play an invaluable role in characterizing soil properties, especially qualitative properties that may elude sensing/computer-based modeling approaches. The elegant solution to this conundrum relies on the synthesis of computer-aided predictive modeling with human insight and knowledge.