Development of a fuzzy model for differentiating peanut plant from broadleaf weeds using image features
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RESEARCH
Development of a fuzzy model for differentiating peanut plant from broadleaf weeds using image features Adel Bakhshipour* and Hemad Zareiforoush
Abstract 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 Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies on training and testing datasets were respectively 95.56% and 93.75% for J48-CFS; 92.78% and 91.67% for REP-CFS; and 93.33% and 92.59% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems. Keywords: Fuzzy logic, Image processing, Peanut, Wavelet transform, Weed detection Background Despite the remarkable progress made in agricultural industry in recent years, weed management is still a challenging and complex problem. There are mainly three methods for weed control in agricultural fields, namely, manual removal, mechanical hoeing, and chemical weed control. Traditional manual removal of weeds is still a common practice in the peanut fields. Hand hoeing is a very effective operation which is carried out properly. However, it is a tedious, extremely labor-intensive and time consuming operation with *Correspondence: [email protected] Department of Agricultural Mechanization Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
adverse health effects. Furthermore, manual weeding through hand tools can only be employed in small-scale farming or in home gardens and it is not a good practice in large scale cultivation [1, 2]. Even though mechanical weeding using inter-row cultivators is frequently used to remove the weeds between rows [3], the presence of weeds within crop rows and very closed to the main plant, makes it very difficult
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