Sebastian Raubitzek and Thomas Neubauer. Machine Learning and Chaos Theory in Agriculture. ERCIM News, 122, July 2020.
Abstract: Machine learning has found its way into agricultural science for analysis and predictions, e.g., of yield or nitrogen status. Results are encouraging, but predictions in agricultural sciences are still tricky because agriculture is a highly complex system, with outcomes depending on a multitude of complex phenomena, such as weather, irrigation and soil properties. We propose future machine learning research in this sector to consider complex systems (chaos theory) and improve machine learning approaches.