Feature decision-making ant colony optimization system for an automated recognition of plant species

In the present paper, an expert system for automatic recognition of different plant species through their leaf images is investigated by employing the ant colony optimization (ACO) as a feature decision-making algorithm. The ACO algorithm is employed to investigate inside the feature search space in order to obtain the best discriminant features for the recognition of individual species. In order to establish a feature search space, a set of feasible characteristics such as shape, morphology, texture and color are extracted from the leaf images. The selected features are used by support vector machine (SVM) to classify the species. The efficiency of the system was tested on around 2050 leaf images collected from two different plant databases, FCA and Flavia. The results of the study achieved an average accuracy of 95.53% from the ACO-based approach, confirming the potentials of using the proposed system for an automatic classification of various plant species

Simple and computationally efficient movement classification approach for EMG-controlled prosthetic hand: ANFIS vs. artificial neural network

The aim of this paper is to propose an exploratory study on simple, accurate and computationally efficient movement classification technique for prosthetic hand application. The surface myoelectric signals were acquired from 2 muscles—Flexor Carpi Ulnaris and Extensor Carpi Radialis of 4 normal-limb subjects. These signals were segmented and the features extracted using a new combined time-domain method of feature extraction. The fuzzy C-mean clustering method and scatter plots were used to evaluate the performance of the proposed multi-feature versus other accurate multi-features. Finally, the movements were classified using the adaptive neuro-fuzzy inference system (ANFIS) and the artificial neural network. Comparison results indicate that ANFIS not only displays higher classification accuracy (88.90%) than the artificial neural network, but it also improves computation time significantly

Hand movements classification for myoelectric control system using adaptive resonance theory

This research proposes an exploratory study of a simple, accurate, and computationally efficient movement classification technique for prosthetic hand application. Surface myoelectric signals were acquired from the four muscles, namely, flexor carpi ulnaris, extensor carpi radialis, biceps brachii, and triceps brachii, of four normal-limb subjects. The signals were segmented, and the features were extracted with a new combined time-domain feature extraction method. Fuzzy C-means clustering method and scatter plot were used to evaluate the performance of the proposed multi-feature versus Hudgins’ multi-feature. The movements were classified with a hybrid Adaptive Resonance Theory-based neural network. Comparative results indicate that the proposed hybrid classifier not only has good classification accuracy (89.09 %) but also a significantly improved computation time

Plant leaf recognition algorithm using ant colony-based feature extraction technique

Plant recognition as a substantial subject of biology has occupied the minds of many botanists throughout the world to concentrate their efforts on the identification of unknown plant species with the aim of protection and other purposes. As a troublesome and gradual process, traditional methods of taxonomy of plants impede a high rate of performance for the taxonomist in this field. In the modern-day, improvements in the fields of artificial intelligence and soft computing have led to the field of automatic plant recognition being considered as a challenging topic due to the various uses of plants in medicine, food and industry. Although many studies have been undertaken to seek out a method that can be applied for the classification of numerous plants, there is still a lack of a highly efficient system for the recognition of a wide range of different plants. The aim of this research is to contribute to the measurement of physiological dimensions of plant leaves by the proposed Auto-Measure algorithm to operate in an automatical manner which inherently requires an improvement in automatic feature extraction. Moreover, the ant colony optimisation technique be applied as an expert algorithm to make a decision for the selection of optimal features in order to enhance the performance of a classifier for recognition of diverse species of plants. To do this, at first, based on the proposed algorithm,the physiological dimensions of leaves are automatically measured and with regard to these parameters, specified features such as shape, morph, texture and colour are extracted from the image of the plant leaf through image processing to create a reserved feature database to be used for different species of plants. Then, based on the characteristics of each species, decision making is done by means of ant colony optimisation as a search algorithm to return the optimal subset of features regarding the related species. Finally, the selected features are employed by a multi-class support vector machine to classify the species. The proposed method was applied to different kinds of plant and herb species for testing the system and it was found from the experimental results that the system, by eliminating redundant features, not only optimised the number of features in the subset, but also had a remarkably positive impact on the performance of the classifier in a way that implementation of the proposed method on almost 2830 leaves improved the average accuracy over all the plant databases to 96.66 %. Therefore, it can be concluded that the proposed method is capable of a high rate of classification of various plant species

Automated recognition of Ficus deltoidea using ant colony optimization technique

Improving in the fields of soft computing and artificial intelligence, the branch study of automated herb recognition among plenty of weeds has become challenging issue due to their applications in medicine, food and industry. This paper presents innovative method to improve the accuracy of classification as well the efficiency, such that irrelevant features that make computational complexity are ignored by feature subset selection that is proposed by means of ant colony optimization algorithm (ACO). At first, through image processing specified features are extracted from the Ficus deltoidea leaves such as vein, morphology and texture features and they construct a search space to be chosen for the optimal subset features that is selected by ACO algorithm as support vector machine (SVM) classify them. The experimental results have shown that the proposed method not only optimize feature subset, but also has a remarkable positive impact on accuracy