Optimization of photosynthetic rate parameters using adaptive neuro-fuzzy inference system (ANFIS)

College

Gokongwei College of Engineering

Department/Unit

Manufacturing Engineering and Management

Document Type

Conference Proceeding

Source Title

2017 International Conference on Computer and Applications, ICCA 2017

First Page

129

Last Page

134

Publication Date

10-20-2017

Abstract

Crop growth is greatly affected by light intensity, temperature and CO2 concentration. The combinations of these factors are considered in growing crops. In this study, a system was developed using adaptive neuro-fuzzy inference system for the prediction of the photosynthetic rate of lettuce crop based on the temperature, light intensity and CO2. A fuzzy inference system is designed to generate the rules for the fuzzy logic where inputs of these are from the output of the trained neural network. Based on the result, the system was able to predict the photosynthetic rate of the lettuce crop based on the three input parameters. The RMSE value for the ANFIS model was found to be 2.7843e-05. © 2017 IEEE.

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Digitial Object Identifier (DOI)

10.1109/COMAPP.2017.8079734

Disciplines

Manufacturing

Keywords

Carbon dioxide; Photosynthesis; Fuzzy logic; Lettuce—Effect of temperature on

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