Artificial Neural Networks Application in order to Predicting of the Apparent Date of Wheat Phenological Stages Using Climatic Data

Prediction of the apparent time of crop phonological stages mainly strategic plants such as, wheat, corn and rice help us to achieve to the exact time in order to control pests, weeds and pathogens and also, to the best time for operating such as, fertilizing and irrigation. The main purpose of such study is to estimate the appearance of phonological stages in dry farming wheat at the time interval of short duration before their occurrence using meteorological data. Recently, the application of Artificial Neural Networks (ANNs) has developed into a powerful tool that can compute most complicated equations and numerical analyses to the best approximation. According to the available data and information from different areas in Iran, this research was accomplished using Sararood station data in Kermanshah Province which has the most complete homogeneous statistics. In this study, the results of climatology for eleven meteorological factors in period (1990-99) including mean daily minimum temperature, extreme daily minimum temperature, mean daily maximum temperature, extreme daily maximum temperature, total daily rainfall, number of rainfall days, sum of sun hours, mean daily wind speed, extreme daily wind speed, mean daily relative humidity and sum of water requirement for each of eleven phonological stages in wheat including sowing, germination, emergence, third leaves, tillering, stem formation, heading, flowering, milk maturity, wax maturity, full maturity were collected separately for each farming year and arranged in two matrices: A matrix whose rows are repetitions of the statistical years (i) at each phenological stages of wheat (j) and the columns are meteorological factors (k). A matrix whose rows form each of the statistical years (i) and the columns are meteorological factors (k) at each phenological stage (j). In fact, statistical years (i), phonological stages (j) and meteorological factors (k) are the basic elements of 3-D matrix (M ijk) arranged as above. Finally, different networks were made for each stage and the optimum values of network parameters were obtained by trial and error. It should be reminded that two of the eight- year farming were randomly excluded from network training computations and that the comparison of the estimated data with the real data for these two years were used to test the accuracy of the models. The model that obtained has the following capabilities: 1. Prediction of the apparent date of seven phonological stages (From tillering till full maturity) with maximum error 2 to12 days which naturally, by moving towards primary stages, the amount of error is increased. 2. Achieving the sensitivity for each phenological stage with respect to particular meteorological factors that helps to understand the effect of decreasing and increasing factors at each stage. Thus, enabling us to minimize and control the harmful effect of each factor at different stages. 3. Recognizing the order of priority and importance of each meteorological factor, in the plant growth and yield, enabling us to eliminate a number of the elements in the data matrix in order to speed up and facilitate the stages for the preparation of the input data and to speed up network responses without affecting the estimates and accuracy significantly.