Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

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.

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

“…In order to extract the colour values in this study, the images of plant regions that obtained from the image preparation section (figure 3d) were converted from RGB colour space to HIS, and L*a*b colour spaces and the average and standard deviation measures of different colour components in these three spaces, namely; Red (R), Green (G), Blue (B), Hue (H), Saturation (S), Intensity (I), Lightness (L), a* and b* colour components, were determined. The corresponding colour space transformation equations are presented by researchers [7,57,58]. A total number of 18 colour features including nine colour averages and nine colour standard deviations were extracted in this case.…”

“…In order to evaluate the performance of the classifiers, three statistical criteria including accuracy (ACC), Cohen kappa statistics (k), Root Mean Squared Error (RMSE), were determined for the developed models. These criteria are detailedly described [7,16,64,77]. Higher values for ACC and k, and lower value of RMSE correspond to better classification performance.…”

“…Accurate weed detection in croplands, as a prerequisite for applying any precision weed management technology [6], is still a challenging step toward the development of site-specific weed control machines, especially when there are intra-row weeds that are highly overlapped with the main plant. Efficient weed removal weather using variable-rate sprayers, or precise mechanical, electrical, or thermal hoeing systems, preliminary requires to detect and segregate weeds from main crop [7]. Computer vision is a well-known approach that has shown to be successful in object recognition and detection in a variety of applications including agriculture automation and monitoring.…”

“…Different supervised and unsupervised classifier algorithms including Artificial Neural Networks (ANN), Support Vector Machine (SVM), Decision Tree (DT), Principal Component Analysis (PCA), Bayesian Classifier (BC), Linear Discriminant Analysis (LDA), K-Means, k-Nearest Neighbors (kNN), etc. have been applied for distinguishing the crops from weeds [7,13,[30][31][32][33][34][35][36][37][38].…”