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Image based leaf segmentation and counting in rosette plants

Praveen Kumar, J., Domnic, S.
Information processing in agriculture 2019 v.6 no.2 pp. 233-246
chlorophyll, data collection, image analysis, leaf area, leaves, plant growth
This paper proposes an efficient method to extract the leaf region and count the number of leaves in digital plant images. The plant image analysis plays a significant role in viable and productive agriculture. It is used to record the plant growth, plant yield, chlorophyll fluorescence, plant width and tallness, leaf area, etc. frequently and accurately. Plant growth is a major character to be analyzed among these plant characters and it directly depends on the number of leaves in the plants. In this paper, a new method is presented for leaf region extraction from plant images and counting the number of leaves. The proposed method has three steps. The first step involves a new statistical based technique for image enhancement. The second step involves in the extraction of leaf region in plant image using a graph based method. The third step involves in counting the number of leaves in the plant image by applying Circular Hough Transform (CHT). The proposed work has been experimented on benchmark datasets of Leaf Segmentation Challenge (LSC). The proposed method achieves the segmentation accuracy of 95.4% and it also achieves the counting accuracy of −0.7 (DiC) and 2.3 (|DiC|) for datasets (A1, A2 and A3), which are better than the state-of-the-art methods.