Development of prediction method for the peak harvest day of persimmon using meteorological data and artificial neural networks

Abstract

In the Gojo Yoshino area of Nara Prefecture, an excellent persimmon production area, a short-term employment plan has been started around six months before harvest time to secure labour for harvesting work. As global warming makes it difficult to predict the harvest time by empirical methods, a new forecasting method is required. In this study, ANN was used to predict the peak harvest date of persimmon based on meteorological data. The three target varieties were ’Tonewase’, ’Hiratanenashi’ and ’Fuyu’. Model parameters were investigated and a model was constructed for each variety. A model was constructed with an error margin of up to three days. In addition, all three varieties are harvested after October, with a maximum error of 2.5 days as at 1 May and 1 June，errors at the 1st of each month up to just before harvest were found to be predictable by a maximum of three days. The adaptability of ANNs as a method for predicting harvest time was demonstrated.