FPGA Based Leaf Chlorophyll estimating regression model

Author

Khan, Md Imran ; Mondol, Raktim Kumar

Author_Institution

Dept. of ICT, Bangladesh Univ. of Eng. & Technol., Dhaka, Bangladesh

fYear

2014

Firstpage

1

Lastpage

6

Abstract

Architecture of simple, portable, low cost Chlorophyll estimator based on Field Programmable Gate Array (FPGA) is proposed in this paper. Color of leaf can give an indication for assessment of plant health and nutrient. Performance analysis of several regression model shows that multivariate linear regression model with nonlinear terms provides best fit between estimated Chlorophyll values with image data. We find that the residuals are near in baseline and the adjusted coefficient of determination (Ra_a² is 0.99 which is very significant. Root mean square error (RMSE) is 3.3 out of 15 leaf image samples with 5 error degrees of freedom (EDF). Hardware architecture is designed as best regression model has less computational complexity and greater accuracy.

Keywords

agriculture; crops; field programmable gate arrays; mean square error methods; regression analysis; FPGA; RMSE; chlorophyll estimator; field programmable gate array; hardware architecture; leaf chlorophyll; multivariate linear regression model; plant health; plant nutrient; root mean square error; Analytical models; Data models; Image color analysis; Mathematical model; Radiation detectors; Random access memory; Registers; FPGA; Leaf Chlorophyll; Regression; Verilog HDL;

fLanguage

English

Publisher

ieee

Conference_Titel

Software, Knowledge, Information Management and Applications (SKIMA), 2014 8th International Conference on

Type

conf

DOI

10.1109/SKIMA.2014.7083557

Filename

7083557