Skip to main content

Advertisement

SpringerLink
Log in

Automated framework for accurate segmentation of leaf images for plant health assessment

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Leaf segmentation is significantly important in assisting ecologists to automatically detect symptoms of disease and other stressors affecting trees. This paper employs state-of-the-art techniques in image processing to introduce an accurate framework for segmenting leaves and diseased leaf spots from images. The proposed framework integrates an appearance model that visually represents the current input image with the color prior information generated from RGB color images that were formerly saved in our database. Our framework consists of four main steps: (1) Enhancing the accuracy of the segmentation at minimum time by making use of contrast changes to automatically identify the region of interest (ROI) of the entire leaf, where the pixel-wise intensity relations are described by an electric field energy model. (2) Modeling the visual appearance of the input image using a linear combination of discrete Gaussians (LCDG) to predict the marginal probability distributions of the grayscale ROI main three classes. (3) Calculating the pixel-wise probabilities of these three classes for the color ROI based on the color prior information of database images that are segmented manually, where the current and prior pixel-wise probabilities are used to find the initial labels. (4) Refining the labels with the generalized Gauss-Markov random field model (GGMRF), which maintains the continuity. The proposed segmentation approach was applied to the leaves of mangrove trees in Abu Dhabi in the United Arab Emirates. Experimental validation showed high accuracy, with a Dice similarity coefficient 90% for distinguishing leaf spot from healthy leaf area.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Barbedo, J. (2016). A novel algorithm for semi-automatic segmentation of plant leaf disease symptoms using digital image processing. Tropical Plant Pathology, 41(4), 210–224.

    Article  Google Scholar 

  • Barbedo, J.G.A. (2017). A new automatic method for disease symptom segmentation in digital photographs of plant leaves. European Journal of Plant Pathology, 147(2), 349–364.

    Article  CAS  Google Scholar 

  • Bauer, S, Korc, F, Förstner, W. (2009). Investigation into the classification of diseases of sugar beet leaves using multispectral images. In E.J. van Henten, D. Goense, C. Lokhorst (Eds.) Precision agriculture (Vol. 9, pp. 229– 238).

  • Besag, J. (1986). On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society Series B (Methodological), 48(3), 259–302.

    Article  Google Scholar 

  • Bouman, C, & Sauer, K. (1993). A generalized gaussian image model for edge-preserving map estimation. IEEE Transactions on Image Processing, 2(3), 296–310.

    Article  CAS  Google Scholar 

  • EAD. (2016). Mangrove in UAE: a unique marine ecosystem. Abu Dhabi Government Portal. https://www.abudhabi.ae/portal/public/en/citizens/safety-and-environment/environment/mangroves-in-uae.

  • Edwards, C, & Penney, D. (1986). Calculus and analytic geometry, 2nd edn. Upper Saddle: Prentice-Hill.

    Google Scholar 

  • El-Baz, A, Elnakib, A, Khalifa, F, El-Ghar, MA, McClure, P, Soliman, A, Gimelrfarb, G. (2012). Precise segmentation of 3-d magnetic resonance angiography. IEEE Transactions on Biomedical Engineering, 59(7), 2019–2029.

    Article  Google Scholar 

  • El-Baz, A, Gimel’farb, G, Suri, JS. (2015). Stochastic modeling for medical image analysis. Boca Raton: CRC Press.

    Book  Google Scholar 

  • Ferreiro-Armán, M, Da Costa, JP, Homayouni, S, Martín-Herrero, J. (2006). Hyperspectral image analysis for precision viticulture. In International conference image analysis and recognition (pp. 730–741): Springer.

  • Food and Agriculture Organization (FAO). (2001). State of the world’s forests. Rome: FAO of the United Nations.

    Google Scholar 

  • Garcia Zapirain, B, Shalaby, A, El-Baz, A, Elmaghraby, A. (2017). Automated framework for accurate segmentation of pressure ulcer images. Computers in Biology and Medicine, 90, 137–145.

    Article  Google Scholar 

  • Gerig, G, Jomier, M, Chakos, M. (2001a). Valmet: a new validation tool for assessing and improving 3d object segmentation. In International conference on medical image computing and computer-assisted intervention (pp. 516–523): Springer.

  • Gerig, G, Jomier, M, Chakos, M. (2001b). Valmet: a new validation tool for assessing and improving 3D object segmentation, (pp. 516–523). Berlin: Springer.

  • Guo, R, Dai, Q, Hoiem, D. (2013). Paired regions for shadow detection and removal. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (12), 2956– 2967.

    Article  Google Scholar 

  • Kovacs, JM, King, J.M.L, Flores de Santiago, F, Flores-Verdugo, F. (2009). Evaluating the condition of a mangrove forest of the mexican pacific based on an estimated leaf area index mapping approach. Environmental Monitoring and Assessment, 157(1), 137–149.

    Article  CAS  Google Scholar 

  • Liao, W, Rohr, K, Kang, CK, Cho, ZH, Wörz, S. (2011). A generative mrf approach for automatic 3d segmentation of cerebral vasculature from 7 tesla mra images. In 2011 IEEE international symposium on biomedical imaging: from nano to macro (pp. 2041–2044).

  • Lipps, PE. (1998). Gray leaf spot: a global threat to corn productions. APSnet Features, https://doi.org/10.1094/APSnetFeature-1998-0598.

  • Lucas, GB, & Campbell, L. (2013). Introduction to plant diseases: identification and management. Berlin: Springer.

    Google Scholar 

  • Nagelkerken, I, Blaber, S, Bouillon, S, Green, P, Haywood, M, Kirton, L, Meynecke, JO, Pawlik, J, Penrose, H, Sasekumar, A, Somerfield, P. (2008). The habitat function of mangroves for terrestrial and marine fauna: a review. Aquatic Botany, 89(2), 155–185. mangrove Ecology – Applications in Forestry and Costal Zone Management.

    Article  Google Scholar 

  • Pethybridge, SJ, & Nelson, SC. (2015). Leaf doctor: a new portable application for quantifying plant disease severity. Plant Disease, 99(10), 1310–1316.

    Article  Google Scholar 

  • Polidoro, BA, Carpenter, KE, Collins, L, Duke, NC, Ellison, AM, Ellison, JC, Farnsworth, EJ, Fernando, ES, Kathiresan, K, Koedam, NE, Livingstone, SR, Miyagi, T, Moore, GE, Nam, VN, Ong, JE, Primavera, JH, Salmo, SG III., Sanciangco, JC, Sukardjo, S, Wang, Y, Yong, JWH. (2010). The loss of species: mangrove extinction risk and geographic areas of global concern. PLoS ONE (4), e10095.

  • Pompelli, M, Antunes, W, Ferreira, D, Cavalcante, P, Wanderley-Filho, H, Endres, L. (2012). Allometric models for non-destructive leaf area estimation of jatropha curcas. Biomass and Bioenergy, 36, 77–85.

    Article  Google Scholar 

  • Powbunthorn, K, Abdullakasim, W, Unartngam, J. (2012). Assessment of the severity of brown leaf spot disease in cassava using image analysis. In Proceedings of international conference of the Thai Society of Agricultural Engineering. Thailand: Chiangmi.

  • Prasad, S, Peddoju, SK, Ghosh, D. (2014). Mobile mixed reality based damage level estimation of diseased plant leaf. In Eighth international conference on next generation mobile apps, services and technologies (NGMAST), (Vol. 2014 pp. 72–77): IEEE.

  • Prasad, S, Peddoju, SK, Ghosh, D. (2016). Multi-resolution mobile vision system for plant leaf disease diagnosis. Signal, Image and Video Processing, 10(2), 379–388.

    Article  Google Scholar 

  • Rumpf, T, Mahlein, AK, Steiner, U, Oerke, EC, Dehne, HW, Plümer, L. (2010). Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 74(1), 91–99.

    Article  Google Scholar 

  • Varma, V, & Osuri, AM. (2013). Black spot: a platform for automated and rapid estimation of leaf area from scanned images. Springer’s Plant Ecology, 214(12), 1529–1534.

    Article  Google Scholar 

  • Viola, P, & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition, 2001. CVPR 2001, (Vol. 1 pp. I–I): IEEE.

  • Wang, X, Zhang, M, Zhu, J, Geng, S. (2008). Spectral prediction of phytophthora infestans infection on tomatoes using artificial neural network (ann). International Journal of Remote Sensing, 29 (6), 1693–1706.

    Article  Google Scholar 

  • Webb, A. (2002). Statistical pattern recognition. New York: Wiley.

    Book  Google Scholar 

  • Zhou, R, Kaneko, S, Tanaka, F, Kayamori, M, Shimizu, M. (2015). Image-based field monitoring of cercospora leaf spot in sugar beet by robust template matching and pattern recognition. Computers and Electronics in Agriculture, 116, 65–79.

    Article  Google Scholar 

  • Zou, KH, Warfield, SK, Bharatha, A, Tempany, CM, Kaus, MR, Haker, SJ, Wells, WM, Jolesz, FA, Kikinis, R. (2004). Statistical validation of image segmentation quality based on a spatial overlap index1: scientific reports. Academic Radiology, 11(2), 178–189.

    Article  Google Scholar 

Download references

Funding

This work is funded by the Office of Research and Sponsored Programs of Abu Dhabi University under grant number 19300068.

Author information

Authors and Affiliations

  1. Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi, United Arab Emirates

    Mohammed Ghazal

  2. Bioengineering Department, University of Louisville, Louisville, KY, USA

    Mohammed Ghazal, Ali Mahmoud, Ahmed Shalaby & Ayman El-Baz

Authors
  1. Mohammed Ghazal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Mahmoud
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmed Shalaby
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ayman El-Baz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammed Ghazal.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghazal, M., Mahmoud, A., Shalaby, A. et al. Automated framework for accurate segmentation of leaf images for plant health assessment. Environ Monit Assess 191, 491 (2019). https://doi.org/10.1007/s10661-019-7615-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-019-7615-9

Keywords

Search

Navigation