All Times ET
Keywords: Categorical Prediction, Transition Probability, Deep Neural Networks, Crop Rotation Patterns
Traditional crop-rotation models suffer from sparsity issues due to the representation of categorical data as indicator variables or parametric spaces that exponentially increase with longer rotation patterns. In this paper, a quantum-inspired neural network model that has the potential of processing longer crop-rotation patterns over larger areas is proposed and compared to two established models.