Optimal light quality and photon flux density-based light requirement real-time dynamic obtaining method

A photon flux density, real-time dynamic technology, applied in neural learning methods, design optimization/simulation, instruments, etc., can solve the problem of not considering the impact of light quality on crop photosynthesis, not considering the temperature coupling relationship, not considering The actual demand of crops, etc.

Active Publication Date: 2017-09-08
NORTHWEST A & F UNIV
View PDF4 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The traditional regulation of light environment is threshold regulation, which cannot meet the requirement of changing the amount of regulation according to changes in the external environment, and does not consider the actual needs of crops.
Hu Jin et al. considered the difference in light intensity required by crops at different temperatures on the basis of traditional light environment regulation, and used artificial intelligence algorithms to construct a light environment regulation model, which considered the optimal photo

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
  • Optimal light quality and photon flux density-based light requirement real-time dynamic obtaining method
  • Optimal light quality and photon flux density-based light requirement real-time dynamic obtaining method
  • Optimal light quality and photon flux density-based light requirement real-time dynamic obtaining method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0089] The implementation of the present invention will be described in detail below in conjunction with the drawings and examples.

[0090] 1. Experimental conditions

[0091] The experiment of this example was carried out in the Intelligent Agriculture Laboratory of the School of Mechanical and Electronic Engineering, North School of Northwest Agriculture and Forestry University (North latitude 34°07'39", East longitude 107°59'50') from November 8, 2016 to December 8, 2016. , 648 meters above sea level). The selection for the experimental plant variety is cucumber, and the variety is "Golden Embryo 98-1F 1 ".

[0092] The swollen seeds were germinated in a petri dish, and low temperature treatment was carried out when they were about to germinate. From October 6, 2016 to November 6, 2016, nutrients were used in 50-hole (540mm×280mm×50mm) plug trays every day. The seedlings are raised once in a pot, and the seedling raising substrate is a special substrate for raising seedl...

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 discloses an optimal light quality and photon flux density-based light requirement real-time dynamic obtaining method. The method comprises the steps of firstly performing modeling based on a photosynthetic rate of a GA-GRNN, and optimizing an expansion speed of a GRNN by utilizing a GA algorithm, wherein related analysis of a predicted value and an actually measured value of a photosynthetic rate prediction model of the GA-GRNN is remarkably superior to that of a GRNN model; and then based on the photosynthetic rate prediction model of the GA-GRNN, performing photosynthetic rate optimization by using a quantum genetic algorithm to obtain corresponding optimal light quality and photon flux density, and building a light environment control target value model by adopting multiple linear regression fitting, wherein determination coefficients of optimal light quality model and photon flux density model are 0.992 and 0.9893 respectively. The photosynthetic rate at each temperature is taken as the actually measured value, and the photosynthetic rate corresponding to the optimal light quality and the optimal photon flux density is taken as the predicted value; a related analysis method is adopted; the determination coefficient is 0.936, the fitting straight slope is 1.012, and the intercept is 0.054; and a result shows that the built coupled light quality and photon flux density control target value model is good in performance.

Description

technical field [0001] The invention belongs to the technical field of modern agricultural intelligent supplementary light, in particular to a real-time and dynamic acquisition method of light demand based on optimal light quality and photon flux density. Background technique [0002] Light provides energy for photosynthesis of crops, and photon flux density and light quality are two main aspects that affect the instantaneous photosynthesis rate of crops. Among them, the photon flux density directly determines the total amount of instantaneous light quanta participating in crop photosynthesis, and directly affects the instantaneous photosynthesis rate of crops; the light quality affects the different physiological effects of crops on crop leaf area expansion and dry matter by adjusting the ratio of red and blue light. Accumulation, increased stem diameter, and chlorophyll content indirectly affect photosynthetic rate. Therefore, light environment regulation not only provide...

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
IPC IPC(8): G06N3/08G06F17/50
CPCG06F30/20G06N3/086
Inventor 张海辉张珍胡瑾辛萍萍王智永张斯威张盼
Owner NORTHWEST A & F UNIV
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