Abstract
A novel method of resultant radial distances, leaf perimeter-based features, and RGB color moments-based leaf classification is proposed in this paper. In the training stage, shape features of plant leaf images are extracted by the resultant radial distances and leaf perimeter-based features; and color features are extracted using RGB color moments. Random forest is constructed with the leaf features where leaf names are the class attribute. In the testing stage, shape feature consisting of resultant radial distances and perimeter-based features, and color feature of the query image is extracted by means of same method. The query leaf image is recognized by the already created random forest in the training stage. The proposed method gives 98% recognition rate, which is similar to state-of-the-art leaf recognition methods. This is mainly due to the resultant radial distances for calculating accurate shape features. The smooth and jagged edges of the leaves are perfectly distinguished by leaf perimeter-based features. RGB moments help to distinguish different colored leaves.
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LeafSnap Dataset. https://leafsnap.com/dataset/. Last accessed 2019/11/02
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Hazra, D., Bhattacharyya, D., Kim, Th. (2021). A Random Forest-Based Leaf Classification Using Multiple Features. In: Bhattacharyya, D., Thirupathi Rao, N. (eds) Machine Intelligence and Soft Computing. Advances in Intelligent Systems and Computing, vol 1280. Springer, Singapore. https://doi.org/10.1007/978-981-15-9516-5_20
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DOI: https://doi.org/10.1007/978-981-15-9516-5_20
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