A method and device for identifying the ear differentiation stage of winter wheat

Abstract

The embodiment of the present invention provides a method and device for identifying the ear differentiation stage of winter wheat. The inventive method includes: obtaining the model parameters of the wheat growth simulation model according to the collected parameters in the winter wheat planting area to be identified, and obtaining the localized wheat growth simulation model ; According to the data of the agricultural meteorological station in the winter wheat planting area to be identified, the first temperature data time series of the winter wheat to be identified is obtained, and the second temperature data time series of the winter wheat to be identified is obtained according to the multi-temporal remote sensing images in the winter wheat planting area to be identified; The data assimilation method is to assimilate the first temperature data time series and the second temperature data time series to obtain the assimilated temperature data time series of winter wheat to be identified; according to the time series of assimilated temperature data and the localized wheat growth simulation model, the winter wheat to be identified is obtained Identification results of panicle differentiation stages in the planting area. The embodiment of the present invention realizes identification of winter wheat panicle differentiation stage on a regional scale.

Description

technical field [0001] The embodiments of the present invention relate to the field of agricultural technology, and in particular to a method and device for identifying the ear differentiation stage of winter wheat. Background technique [0002] As an indicator reflecting the growth and development process of crops, crop growth period plays an important role in determining potential yield. Real-time grasp of the crop development process can provide useful information and scientific basis for field management at each growth stage, and is of great significance for achieving stable and high grain yields. In recent years, with the in-depth research on the basic theory of plant physiology and ecology, crop growth models based on the mechanisms of crop photosynthesis, respiration, transpiration, and nutrition have gradually developed. The crop growth models rely on their internal physiological processes and dynamic mechanisms. It can more accurately simulate the growth and develo...

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Problems solved by technology

For a long time, the temperature data driving the operation of the crop model has been mainly obtained through fixed-point long-term observation meteorological stations distributed in the study area. This method has the following two defects: On the one hand, due to the limitation of economic and The number of observing meteorological stations is limited and the spatial distribution is uneven. The temperature data set obtained by the observations can only reflect the temperature changes within a certain range near the station, and cannot express the characteristics of temperature changes at points other than non-observing stations.

Although the temperature value of unknown points can be estimated by using spatial interpolation method, due to the influence of terrain, density and distribution of sample points and interpolation method, the error of interpolation result is relatively large

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