Analysis and prediction of cranberry growth with dynamical neural network models

Abstract

Cranberry plants are very sensitive to weather and other conditions. In this paper, the condition of cranberry growth is analyzed through PCA (principle component analysis) of the minimum cranberry spectral match measurement data. Three neural network models are applied to the one-month ahead prediction. The simulation results show the high performance modeling ability of these neural networks. The reliable prediction provided by the dynamic neural networks will be useful for the farmers to monitor and control the cranberry growth process.