Optimization method of crop growth period model variety parameters based on constraint knowledge and elite individual strategy genetic algorithm

A genetic algorithm, a constrained technology, applied in the fields of genetic laws, genetic models, computing, etc., can solve problems such as unclear biological meaning

Inactive Publication Date: 2019-08-06
NANJING AGRICULTURAL UNIVERSITY
View PDF5 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method effectively integrates the knowledge of variety characteristics with the improved elite strategy genetic algorithm, and solves the problems of multi-peak nonlinearity of the model algorithm and unclear biological significance of the optimization results of the evolutionary algorithm when the parameters of the crop growth period model are automatically corrected. Accurately Estimating Variety Parameters of Crop Growth Period Models

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Optimization method of crop growth period model variety parameters based on constraint knowledge and elite individual strategy genetic algorithm
  • Optimization method of crop growth period model variety parameters based on constraint knowledge and elite individual strategy genetic algorithm
  • Optimization method of crop growth period model variety parameters based on constraint knowledge and elite individual strategy genetic algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment approach

[0089] The WheatGrow wheat growth period model of Nanjing Agricultural University was used to simulate the phenological changes of wheat variety Jinan No. 13 planted in Jining City, Jiaozhou City, Weifang City of Shandong Province and Xuzhou City of Jiangsu Province. combined with figure 1 , to specifically illustrate this embodiment: Step 1: Data preparation for crop growth period model variety parameter optimization

[0090] Select the historical data of Jinan No. 13 wheat variety planted in Jining City, Jiaozhou City, Weifang City, Shandong Province, and Xuzhou City, Jiangsu Province. The experimental site and year data are shown in Table 1. Each set of data records the flowering and maturity stages. Date of actual measurement, see attached Figure 5-1 . Meteorological data are all from local weather stations, including daily maximum temperature (°C), daily minimum temperature (°C), radiation (MJ / m2)), etc., see attached Figure 5-2 with 5-3 . Sowing depth is 2.5cm.

...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention brings forward a constraining knowledge and elite individual strategy genetic algorithm fusion-based crop growth period model variety parameter optimization method and belongs to the field of crop model variety parameter optimization methods. The method comprises the following steps: data preparation is made for crop growth period model variety parameter optimization; an initial range of crop growth period model variety parameters is graded; a phonological period which has key constraints for the model variety parameters are determined, a measured data range of the phonological period having key constraints is estimated, an improved genetic algorithm is set to control initial parameter value, an initial species population is generated, a fitness function is built, gene direction factors of individuals are built, a selection operator is built via a roulette method, a direction guide crossover operator is built, a direction and elite individual guide mutation operator is built, and elite individual-based local search is conducted. The constraining knowledge and elite individual strategy genetic algorithm fusion-based crop growth period model variety parameter optimization method can help address problems of low efficiency, inconsistency between optimization results and variety biology, and the like; parameter optimization accuracy and parameter optimization efficiency are improved, and the method has scientific significance and practical value.

Description

[0001] 1. Technical field [0002] The invention belongs to the field of agricultural information technology, and is the intersecting field of evolutionary algorithm guided by domain knowledge and crop growth model. The invention relates to an automatic optimization method for estimating variety parameters of a nonlinear, discontinuous and multi-parameter crop growth period mechanism model, which can be used for cereal crops such as wheat and rice. Specifically, this method is a method for optimizing the variety parameters of crop growth period models based on the fusion of constrained knowledge and elite individual strategy genetic algorithm. [0003] 2. Background technology [0004] Climate change has become a hot topic of concern to the international community and has been listed as one of the top ten environmental issues in the world. In the early 1980s, scientists began to see significant changes in the global climate. In 2007, the Fourth Assessment Report released by t...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Patents(China)
IPC IPC(8): G16B10/00G06N3/12
CPCG06N3/126G06Q50/02G16B25/00
Inventor 姜海燕许一骅庄嘉祥刘蕾蕾朱艳于娟娟吴冕
Owner NANJING AGRICULTURAL UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap