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Article type: Research Article
Authors: Pushpa, B.R. | Shobha Rani, N.; *
Affiliations: Department of Computer Science, Amrita School of Arts and Sciences, Mysuru Campus, Amrita Vishwa Vidyapeetham, India
Correspondence: [*] Corresponding author. N. Shobha Rani, Department of Computer Science, Amrita School of Arts and Sciences, Mysuru Campus, Amrita Vishwa Vidyapeetham, India. E-mail: n_shobharani@my.amrita.edu.
Abstract: Low resolution mobile photographed images pose a complex set of research challenges as compared to non-mobile captured images, which really is a significant issue these days. For non-mobile captured and high-resolution photos, current plant recognition systems are the best solution providers. This study proposes the identification and extraction of leaf regions from complex backgrounds to meet the automatic recognition needs of a variety of mobile phone users. Additionally multiple factors complicate the leaf region extraction from complex backgrounds such as varying background patterns, clutters, varying leaf shape/size and varying illumination due to volatile weather conditions. In this paper, a simple and efficient method for leaf extraction from complex background of mobile photographed low resolution images is proposed based on color channel thresholding and morphological operations. A self-built database of 5000 mobile photographed images in realistic environments is adapted for experimentations. Experiments were conducted on various resolution categories, and it was discovered that the proposed model has an average dice similarity measure of 99.5 percent for successful extraction of the leaf region in 13MP mobile photographed images. Furthermore, our comparative investigation reveals that the suggested model outperforms both traditional and state-of-the-art techniques.
Keywords: Leaf extraction, color thresholding, morphological operations, realistic backgrounds, mobile camera images, gradient image analysis
DOI: 10.3233/JIFS-212451
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 773-789, 2022
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