An Interactive Web Application for Predicting Ripening of Bilberry (Vaccinium myrtillus) in Finland

Abstract

I used a temperature-sum model to predict the ripening of bilberries (Vaccinium myrtillus) on a daily basis, using real-time temperature data. The temperature-sum model was developed by fitting to historical data on berry ripening, and by finding a parameter combination that minimizes the model prediction error. The model was then used to predict the timing of berry ripening by using temperature data for a 10 × 10-km grid that covered the entire of Finland. The berry ripening predictions were presented online on a map showing the berry ripening predictions in real time. One third of the users indicated that the model predictions were correct, and another third pointed out that the first berries were already ripened when the model predicted ripening to take place. I, therefore, conclude that the model predicts the bilberry ripening with good accuracy.