Prediction of crop phenology — A component of parallel agriculture management

Accurate prediction of crop development stages (phenolgoy) plays an important role in crop production management. Many biochemical actions of plants change with their development stage, therefore, fertilizing the plants at plants' need can increase the fertilizer use efficiency and decrease the waste. For this purpose, this paper presents a prediction model of crop phenology, describing development stages of crops as a function of the most important environmental factors, temperature and photoperiod. Machine learning is used to calibrate the crop phenology according to the predicted temperature one month later. The results indicated that our method can improve the prediction precision. The resulting model will be incorporated in a crop production management system platform developed for agriculture.