Improvement And Application Of Hurst Index Algorithms For Crop Growth Monitoring

Crop growth is the state and trend of crop growth.Growth monitoring can provide fast and macroscopic information for field management and early yield estimation,which is of great significance.Crop growth monitoring has become an important part of agricultural remote sensing research since the focus of agricultural remote sensing monitoring shifted from remote sensing to agricultural production process monitoring in the 1990 s.The purpose of monitoring crop growth is to monitor crop growth and take corresponding measures to improve crop yield.Generally speaking,there are many methods to monitor crop growth,such as monitoring maize growth by UAV,monitoring crop diseases and pests by remote sensing calculation,etc.Most of them are monitoring crop growth period,and few of them are analyzing and studying crop growth trend.Studying crop growth is conducive to controlling crop growth process,making effective crop planting plans ahead of time,and actively preparing for possible problems encountered in the process of crop growth.Vegetation index is a simple measurement of the vegetation condition on the surface.The change of crop growth can be expressed by the change of vegetation index.The vegetation index corresponding to each period of crop growth can construct a time series of vegetation index.Because the growth of crops in the earlier period affects the growth of crops in the later period,the time series constructed by vegetation index is a memory sequence.Hurst index can be used to analyze time series with long-term memory characteristics.So Hurst index can be used to analyze the time series of vegetation index.Vegetation growth information can be obtained by analyzing the change of vegetation index.Hurst index is traditionally taken as positive real number.This paper proposes to improve Hurst index by adding imaginary part on the basis of traditional Hurst index.The imaginary part can be positive and negative.The improved Hurst index can not only describe the trend of time series change with Brownian motion,but also describe the continuity of time series change.It can predict the change of crop growth more comprehensively.The improved Hurst index can describe the growth trend of crops,reflect the continuity of crop growth trend,maintain the current strength of this growth trend,and achieve the purpose of forecasting growth trend.By using the improved Hurst index to analyze the national vegetation index and the vegetation index in the West and east of Heihe-Tengchong line,the prediction results are compared with the actual vegetation change.The results show that the improved Hurst index has obvious effect in predicting the vegetation index.In order to further verify the effect of Hurst index in crop growth prediction,this paper uses Hurst index to analyze the vegetation index of maize.The experimental results show that Hurst index can analyze the current growth trend of maize.Finally,the improved Hurst index was applied to the prediction of lettuce growth.By analyzing the growth of lettuce on the 10 th,14th,18 th and 23 rd days,the growth of lettuce on the 11 th,15th,19 th and 24 th days could be predicted respectively.The experimental results show that the improved Hurst index is effective in predicting lettuce growth.In the experiment,lettuce was divided into two experimental areas.The maize growth in one of the experimental areas was analyzed by improved Hurst index.When the growth of lettuce was predicted to weaken,the corresponding measures were formulated and implemented.Comparing the lettuce yields of the two experimental areas,it was found that the yield of crops was improved by improving Hurst index to predict the growth trend of lettuce for production intervention.In conclusion,the combination of Hurst index,improved Hurst index and crop growth monitoring platform is beneficial to grasp crop conditions and improve crop yield.