Journal article

Medicinal Plant Recognition of Leaf Shape using Localized Arc Pattern Method

NI KADEK AYU WIRDIANI ANAK AGUNG KOMPIANG OKA SUDANA

Volume : 8 Nomor : 4 Published : 2016, July

International Journal of Engineering and Technology (IJET)

Abstrak

Medicinal plants are plants that have benefit in order to supply the needs of families traditionally medicine. Medicinal plants have diverse types that causing modern society have difficulty in recognizing these crops. Medicinal plants generally can be identified by the leaves, stems and fruit. One of the leaves characteristics can be distinguished based on vein structure and shape of its. Based on these problem, plant recognition based on vein and shape are made by using Localized Arc Pattern Method. There are two important processes in Plant Recognition Applications. First process is Enrollment and the second is Recognition process. In the Enrolment process, the leaves image filed as many as 6 images for each leaves type. This image then calculated based on the 42 special model pattern obtained and the feature is stored as a reference image. Leaves images that used as test image are 200 images. On the Recognition process, the test image will be process which as same as at Enrollment process, however feature from the test image will be comparing with reference image in database, then it calculate the difference value. This process uses a threshold value to determine whether the test images leaves are recognized or not. When dissimilarity value is smaller than the threshold is known as the same leaves, when instead then it known as a different leaves or not known at all. Experiment result shows in this application can recognize 77% of total leaves and False Accepted Ratio (FAR) equal to 4.5% and False Rejection Ratio (FRR) equal to 18.5%. This result was influenced by the shiny surface of leaf and shape of the leaves are small. Keyword: Medicinal Plant Recognition, Leaf Recognition, Localized Arc Pattern, feature extraction, matching process, dissimilarity value, FAR, FRR.