Labview-based Study on the Modeling Method of Chlorophyll Content Prediction in Tomato Leaves

Labview-based Study on the Modeling Method of Chlorophyll Content Prediction in Tomato Leaves

Ji QianJuan Zhou Yang Liu

College of Horticulture, Hebei Agricultural University, Baoding 071000, China

College of Mechanical & Electrical Engineering, Hebei Agricultural University, Baoding 071000, China

Department of Software Engineering, Software Institute of Hebei, Baoding 071000, China

Corresponding Author Email: 
qianji167@163.com
Page: 
413-425
|
DOI: 
https://doi.org/10.18280/ama_b.600211
Received: 
9 May 2017
|
Accepted: 
25 May 2017
|
Published: 
30 June 2017
| Citation

OPEN ACCESS

Abstract: 

The traditional measuring method of chlorophyll content is cumbersome and time-consuming. Taking the labview software and IMAQ-Vision toolkit as the platform and the tomato leaves as test materials, this paper adopts the computer vision technology to extract the component value of the tomato leaf image under different color spaces and employs the statistical analysis method to establish the correlation and regression equation between the image component and the chlorophyll content. It is obtained that the regression equation between the SPAD value and the leaf color characteristic parameter H/(S+L) is y=0.0003x2-0.0139x+0.3411, whose maximum coefficient of determination is R2=0.7327. It is indicated that the method is effective and feasible for the prediction of tomato chlorophyll and also lays the foundation for the development of crop growth monitoring instrument.

Keywords: 

Tomato leaf, Chlorophyll, Computer vision, Labview

1. Introduction
2. Materials and Methods
3. Results and Analysis
4. Discussion
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