Method for Measuring the Similarity of Multiple Metrological Sequences in the Key Phenological Phase of Rice-based on Dynamic Time

The automatic classification of historical data of myriad diverse meteorological sequences in the annual period can help to find the climate differences through key phenology of rice. In this paper, a hybrid gradients-shape dynamic time warping (HGSDTW) algorithm is proposed to measure the similarity of meteorological data during the diverse rice growth period at various locations. The weighting calculation of Euclidean distance uses the form factor in the rice jointing and heading stage. The distance matrix constructs first & second-level gradient single-factor transformation sequences during the period. The dynamic programming method obtains the similarity distances of single and multiple meteorological factors. The results show that the classification accuracy rate from HGSDTW of the heading & jointing stage is higher than that of other similar algorithms. Furthermore, it can observe that the clustering number increases the classification accuracy, and the HGSDTW algorithm maintains the accuracy of 14% for varieties of rice at diverse locations to multiple years of jointing. Besides, the automatic classification experiment of sequence period shows that the classification accuracy of this method is higher than that of another similarity measure. The classification accuracy rate of the heading stage sequence is 10%~14% higher than that of a similar previous standard measurement algorithm, and the jointing period is 1%~9% higher. In this case, the cluster number increasing the classification accuracy, and the HGSDTW maintain the overall accuracy of 14%. Thus, this method can be effectively combined with the classification algorithm to improve the efficiency of the automatic classification of multi-weather sequence data in key phenological periods of rice.