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.
Highlights
IntroductionThere are mainly three methods for weed control in agricultural fields, namely, manual removal, mechanical hoeing, and chemical weed control
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
The classification accuracy, Root Mean Squared Error (RMSE) and kappa statistics of J48-Correlation-based Feature Selection (CFS) tree were respectively obtained as 80.56%, 0.2947, and 0.73, when the tree was evaluated on test dataset
Summary
There are mainly three methods for weed control in agricultural fields, namely, manual removal, mechanical hoeing, and chemical weed control. Hand hoeing is a very effective operation which is carried out properly. It is a tedious, extremely labor-intensive and time consuming operation with. Increasing the Bakhshipour and Zareiforoush Plant Methods (2020) 16:153 cost of agriculture, environmental pollution, and negative effect on the human health are the major drawbacks associated with uniform application of herbicides [5, 6]. Variable rate application of herbicides, based on the presence and intensity of weeds in different parts of fields, can make it possible to optimally use chemicals, which leads to reduced cost and minimal environmental contamination
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