Abstract
The paper proposes a novel method to segment disease spots in leaves using image processing techniques. In the process of disease spot segmentation many challenges are faced such as uneven illumination and clutter background. To solve uneven illumination, color spaces and gray scale conversions are summed. Color spaces like H (hue) component of HSV, L*a*b* color spaces and Excess Red index (ExR) are used. Color to gray scale conversion is done by using weighted mulitresolution channel. Region growing method is used to solve the clutter background issues by interactively selecting growing seeds under real field conditions. Using precision, performance measure is calculated and an average segmentation accuracy of 94% is achieved.
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References
https://imagedatabase.apsnet.org/(APS Image Dataset, An Image Database and Educational Resource (n.d.). American Phytopathological Society, St. Paul, MN)
Burt P, Adelson E (1983) The Laplacian pyramid as a compact image code. IEEE Trans Commun 31(4):532–540
Burt, Peter J, and Raymond J. Kolczynski. (1993). “Enhanced image capture through fusion.” In 1993 (4th) International Conference on Computer Vision, pp. 173–182. IEEE
Dhingra G, Kumar V, Joshi HD (2018) Study of digital image processing techniques for leaf disease detection and classification. Multimed Tools Appl 77:19951–20000. https://doi.org/10.1007/s11042-017-5445-8
Grundland M, Dodgson NA (2007) Decolorize: fast, contrast enhancing, color to grayscale conversion. Pattern Recogn 40(11):2891–2896
Joseph Abraham Sundar K, Vaithiyanathan V, Manickavasagam M, Sarkar AK (2015) Enhanced singular value decomposition based fusion for super resolution image reconstruction. Def Sci J 65(6):459–465
Jothiaruna N, Joseph K, Sundar A (2019) A segmentation method for comprehensive color feature with color-to-Grayscale conversion using SVD and region-growing method. First International Conference on Sustainable Technologies for Computational Intelligence, Rajasthan, pp 303–310. https://doi.org/10.1007/978-981-15-0029-9_24
Jothiaruna, N K Joseph Abraham Sundar (2019). “Survey on Diseased Leaf Using Segmentation”, International conference on intelligent sustainable systems (ICISS), Coimbatore, : 225–230, https://doi.org/10.1109/ISS1.2019.8908017.
Jothiaruna N, Sundar KJA, Karthikeyan B (2019) A segmentation method for disease spot images incorporating chrominance in comprehensive color feature and region growing. Comput Electron Agric 165:1–8
Kim Y, Jang C, Demouth J, Lee S (2009) Robust color-to-gray via nonlinear global mapping. ACM Transactions on Graphics (TOG) 28(5):161
Lu C, Xu L, Jia J (2014) Contrast preserving decolorization with perception-based quality metrics. Int J Comput Vis 110(2):222–239
Ma J, Keming D, Zhang L, Zheng F, Chu J, Sun Z (2017) A segmentation method for greenhouse vegetable foliar disease spots images using color information and region growing. Comput Electron Agric 142:110117
Martin M, Nguyen T, Yousefi S, Li B (2019) Comprehensive features with randomized decision forests for hand segmentation from color images in uncontrolled indoor scenarios. Multimed Tools Appl 78:20987–21020. https://doi.org/10.1007/s11042-019-7445-3
Minervini M, Abdelsamea MM, Tsaftaris SA (2014) Image-based plant phenotyping with incremental learning and active contours. Ecological Informatics 23:35–48
Olszewska JI (2015) Active contour based optical character recognition for automated scene understanding. Neurocomputing 161:65–71
M Quinn and JI Olszewska (2019), “British Sign Language Recognition In The Wild Based On Multi-Class SVM,” 2019 Federated Conference on Computer Science and Information Systems (FedCSIS), Leipzig, Germany: 81–86, https://doi.org/10.15439/2019F274.
Ren S, Lu H, Yuan P, Xue W, Xu HL (2016) Segmentation algorithm of cucumber leaf disease image based on saliency detection. Transactions of the Chinese Society of Agricultural Machinery 47:11–16
Sowmya V, Govind D, Soman KP (2017) Significance of incorporating chrominance information for effective color-to-grayscale image conversion. SIViP 11(1):129–136
Toet A, Van Ruyven LJ, Valeton JM (1989) Merging thermal and visual images by a contrast pyramid. Opt Eng 28(7):287789
Wu T, Toet A (2014) Color-to-grayscale conversion through weighted multiresolution channel fusion. Journal of Electronic Imaging 23(4):043004
Yuan Y, Li M, Chen S, Jiang H (2013) Segmentation of cucumber leaf disease images with complex background. Transactions of the Chinese society for agricultural Machinery 44(10):233–237
Zhou R, Kaneko S’i, Tanaka F, Kayamori M, Shimizu M (2014) Disease detection of Cercospora leaf spot in sugar beet by robust template matching. Comput Electron Agric 108:58–70
Zhu W, Hu R, Liu L (2014) Grey conversion via perceived-contrast. Vis Comput 30(3):299–309
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Jothiaruna, N., Joseph Abraham Sundar, K. & Ifjaz Ahmed, M. A disease spot segmentation method using comprehensive color feature with multi-resolution channel and region growing. Multimed Tools Appl 80, 3327–3335 (2021). https://doi.org/10.1007/s11042-020-09882-7
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DOI: https://doi.org/10.1007/s11042-020-09882-7