Manuscript received September 5, 2022; revised October 5, 2022; accepted November 15, 2022.
Abstract—This paper presents a new approach to plant classification by using leaf edge feature combination with Morphological Transformations and defining key points on leaf edge with SIFT. There are three steps in the process. Image preprocessing, feature extraction, and image classification. In the image preprocessing step, image noise is removed with Morphological Transformations and leaf edge detect with Canny Edge Detection. The leaf edge is identified with SIFT, and the plant leaf feature was extracted by CNN according to the proposed method. The plant leaves are then classified by random forest. Experiments were performed on the PlantVillage dataset of 10 classes, 5 classes of healthy leaves, and 5 classes of diseased leaves. The results showed that the proposed method was able to classify plant species more accurately than using features based on leaf shape and texture. The proposed method has an accuracy of 95.62%.
Keywords—plant species classification, leaf edge, SIFT, random forest, morphological transformations
Cite: Jiraporn Thomkaew and Sarun Intakosum, "Plant Species Classification Using Leaf Edge Feature Combination with Morphological Transformations and SIFT Key Point," Journal of Image and Graphics, Vol. 11, No. 1, pp. 91-97, March 2023.
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