A Model of Plant Identification System Using GLCM, Lacunarity And Shen Features
This work addresses plant identification for applications like botany or agriculture, but it is incremental as it builds on existing feature extraction methods.
The researchers tackled plant identification by developing a new approach combining Gray-Level Co-occurrence Matrix, lacunarity, and Shen features with a Bayesian classifier, achieving accuracies of 97.19% on the Flavia dataset and 95.00% on the Foliage dataset, outperforming other methods.
Recently, many approaches have been introduced by several researchers to identify plants. Now, applications of texture, shape, color and vein features are common practices. However, there are many possibilities of methods can be developed to improve the performance of such identification systems. Therefore, several experiments had been conducted in this research. As a result, a new novel approach by using combination of Gray-Level Co-occurrence Matrix, lacunarity and Shen features and a Bayesian classifier gives a better result compared to other plant identification systems. For comparison, this research used two kinds of several datasets that were usually used for testing the performance of each plant identification system. The results show that the system gives an accuracy rate of 97.19% when using the Flavia dataset and 95.00% when using the Foliage dataset and outperforms other approaches.