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Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features
[post]
2020
unpublished
A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error
doi:10.21203/rs.3.rs-41675/v1
fatcat:qvopl2fqkbhybogzxmxtxs4vnu