Mathematical modeling for active and dynamic diagnosis of crop diseases based on Bayesian networks and incremental learning

To achieve rapid and precise diagnosis of crop diseases, an active and dynamic method of diagnosis of crop diseases is needed and such a method is proposed in this paper. This method adopts Bayesian networks to represent the relationships among the symptoms and crop diseases. This method has two main differences from the existing diagnosis methods. First, it does not use all the symptoms in the diagnosis, but purposively selects a subset of symptoms which are the most relevant to diagnosis; the active symptom selection is based on the concept of a Markov blanket in a Bayesian network. Second, a specific incremental learning algorithm for Bayesian networks is also proposed to make the diagnosis model update dynamically over time in order to adapt to temporal changes of environment. Furthermore, the diagnosis results can be calculated without inference in Bayesian networks, so the method has low time complexity. Theoretical analysis and experimental results demonstrate that the proposed method can significantly enhance the performance of crop disease diagnosis.