Abstract
Plant disease is known as the largest intimidation, which directly affects on production and causes economic loss. Traditionally, human experience is used in defining the class of plant disease, but this often leads to wrong recognition. Thanks to the rapid development of modern technology, many machine-based methods with high efficiency and accuracy can be easily developed. Recently, computer vision has been widely used in various applications which have become intelligent supporters of human. This paper proposes an image-based method using Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) feature extraction for plant disease recognition. Contrast enhancement is used to increase the visual quality in the pre-processing stage. Finally, the classification is done by Support Vector Machine (SVM), and different schemes are used to justify the performance. Overall, the obtained result demonstrates that the proposed approach can identify six classes of plant-based on leaves.
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Trang, K., TonThat, L., Nguyen, KL., Tran, GH. (2022). Identification of Plant Disease Based on Multi-feature Extraction. In: Van Toi, V., Nguyen, TH., Long, V.B., Huong, H.T.T. (eds) 8th International Conference on the Development of Biomedical Engineering in Vietnam. BME 2020. IFMBE Proceedings, vol 85. Springer, Cham. https://doi.org/10.1007/978-3-030-75506-5_44
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DOI: https://doi.org/10.1007/978-3-030-75506-5_44
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