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Quantification of crop growth phenotypic parameters and yield correlation analysis based on vision

A correlation analysis and crop technology, applied in the agricultural field, can solve the problems of few parameters for analysis, unable to meet the requirements of automation, lack of leaf structure parameters, etc., and achieve the effect of reducing analysis cost and analysis time.

Active Publication Date: 2019-01-04
INNER MONGOLIA UNIVERSITY
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Problems solved by technology

[0008] In terms of corn characterization parameters, the analysis of the growth and variation of the height and width of corn plants was completed by manual measurement; The analysis is still in the manual measurement stage and cannot meet the requirements of automation

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  • Quantification of crop growth phenotypic parameters and yield correlation analysis based on vision
  • Quantification of crop growth phenotypic parameters and yield correlation analysis based on vision
  • Quantification of crop growth phenotypic parameters and yield correlation analysis based on vision

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Embodiment Construction

[0068] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0069] The application principle of the present invention will be further described below in conjunction with the accompanying drawings.

[0070] like figure 1 As shown, the vision-based crop growth phenotype parameter quantification and yield correlation analysis method provided by the embodiment of the present invention includes the following steps:

[0071] S101, it is proposed to embed f(p) into the GrabCut algorithm for corn image segmentation. Compared with the watershed algorithm and the mean shift algorithm, this algorithm has a more accurate segmentation effect;

[0072] S102, designing a thinning algorithm to th...

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Abstract

The invention belongs to the technical field of agriculture, and discloses a method for quantifying crop growth potential phenotypic parameters and analyzing yield correlation based on vision, which provides more ideas for agricultural analysis through continuous breakthroughs in image processing, wireless sensors, artificial intelligence and other technologies, and makes traditional manual agriculture and mechanized agriculture develop towards modern intelligence and refinement agriculture. As one of that important analytical subject in the agricultural field, the intelligent and refined agriculture has important significance in the field of plant virtual, yield prediction, garden design and the like. It is of great significance to analyze the characterization parameters and morphologicalcharacteristics in the process of crop growth. On the one hand, the method can predict the relevant parameters of crops, which can guide the allocation of breeding environment; virtual plant growth technology can simulate the plant growth state at any time, so as to reduce the analysis time and reduce the analysis cost.

Description

technical field [0001] The invention belongs to the technical field of agriculture, and in particular relates to a vision-based method for quantifying crop growth phenotype parameters and yield correlation analysis. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: [0003] Due to the small amount of calculation and the relatively simple measurement principle, monocular measurement technology is widely used in the fields of vehicle distance measurement, drone navigation, intelligent robot navigation, etc. The most traditional method is to place a ruler next to the object to be measured, and use the actual size of the ruler to estimate the size of the object to be measured. In recent years, as the analysis of monocular measurement technology has become more and more in-depth, analysts have proposed many excellent monocular measurement methods. Wu Juncai uses multi-scale block binary mode, Adaboost algorithm and...

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Application Information

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IPC IPC(8): G06T7/60G06T7/10
CPCG06T7/60G06T2207/10004G06T2207/20152G06T7/10
Inventor 张志斌侯帅民
Owner INNER MONGOLIA UNIVERSITY
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