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

机译：特征决策蚁群优化系统，用于植物物种的自动识别

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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. (C) 2014 Elsevier Ltd. All rights reserved.
机译：在本文中，通过使用蚁群优化（ACO）作为特征决策算法，研究了一种通过其叶片图像自动识别不同植物物种的专家系统。 ACO算法用于在特征搜索空间内进行研究，以获得用于识别单个物种的最佳判别特征。为了建立特征搜索空间，从叶子图像中提取了一组可行的特征，例如形状，形态，纹理和颜色。支持向量机（SVM）使用选定的特征对物种进行分类。在从两个不同的植物数据库FCA和Flavia收集的大约2050张叶片图像上测试了该系统的效率。该研究结果从基于ACO的方法中获得了95.53％的平均准确度，证实了使用建议的系统对各种植物物种进行自动分类的潜力。 （C）2014 Elsevier Ltd.保留所有权利。

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