Fuzzy classifier of paddy growth stages based on synthetic MODIS data

This paper presents the development of a fuzzy model for classification of paddy growth stages based on synthetic MODIS data. Classification of growth stages is an important process in prediction of crop production using a remote-sensing technology. The proposed approach takes advantages of the nature of a fuzzy system which is able to capture gradual changes/movements by fitting its membership functions. A novel approach to shaping fuzzy input membership functions based on box-plot parameters is also presented. The developed fuzzy model was build and tested on 3935 sets of synthetic MODIS data. The results show that the proposed method was able to classify the growth stages satisfactorily and was robust to handle noises in the data.