An Identification of Chlorophyll Content using Image Processing Technique and Fuzzy Mamdani Method

Background/Objectives: To improve an identification of chlorophyll content in leaf, this paper presents an implementation of a supervised learning method based on membership function training in the context of mamdani fuzzy models. Methods/Statistical Analysis: this paper presents a fuzzy rule based algorithm; natural color image in leaf (Red, Green and Blue) is used as the input to the fuzzy mamdani models. The output of the fuzzy mamdani models is value of chlorophyll content. Results: The proposed approach was superior to identification of chlorophyll content in leaf using image processing technique and mamdani fuzzy method has higher identification accuracy. Conclusion/Application: Finally, the basic difference of value of chlorophyll content between fuzzy mamdani models and the actual was less than 3,1 % on average.