An Algorithm for Plant Disease Visual Symptom Detection in Digital Images Based on Superpixels

Itamar F. Salazar-Reque (1), Samuel Gustavo Huamán (2), Guillermo Kemper (3), Joel Telles (4), Daniel Diaz (5)
(1) Instituto Nacional de Investigación y Capacitación de Telecomunicaciones INICTEL-UNI (National Institute of Research and Training in Telecommunications, National University of Engineering - UNI)
(2) Instituto Nacional de Investigación y Capacitación de Telecomunicaciones INICTEL-UNI (National Institute of Research and Training in Telecommunications, National University of Engineering - UNI)
(3) Instituto Nacional de Investigación y Capacitación de Telecomunicaciones INICTEL-UNI (National Institute of Research and Training in Telecommunications, National University of Engineering - UNI)
(4) Instituto Nacional de Investigación y Capacitación de Telecomunicaciones INICTEL-UNI (National Institute of Research and Training in Telecommunications, National University of Engineering - UNI)
(5) Instituto Nacional de Investigación y Capacitación de Telecomunicaciones INICTEL-UNI (National Institute of Research and Training in Telecommunications, National University of Engineering - UNI)
Fulltext View | Download
How to cite (IJASEIT) :
Salazar-Reque, Itamar F., et al. “An Algorithm for Plant Disease Visual Symptom Detection in Digital Images Based on Superpixels”. International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 1, Feb. 2019, pp. 194-03, doi:10.18517/ijaseit.9.1.5322.
Quantifying diseased areas in plant leaves is an important procedure in agriculture, as it contributes to crop monitoring and decision-making for crop protection. It is, however, a time-consuming and very subjective manual procedure whose automation is, therefore, highly expected. This work proposes a new method for the automatic segmentation of diseased leaf areas. The method used the Simple Linear Iterative Clustering (SLIC) algorithm to group similar-color pixels together into regions called superpixels. The color features of superpixel clusters were used to train artificial neural networks (ANNs) for the classification of superpixels as healthy or not healthy. These network parameters were heuristically tuned by choosing the network with the best classification performance to obtain the automatic segmentation of the diseased areas. The performance of the classifier was measured by comparing its automatic segmentations with those manually made from a database with public and private images divided into nine groups by visual symptom and plant. The mean error of the area obtained was always below 11%, and the average F-score was 0.67, which is higher than that found by the other two approaches reported in the literature (0.57 and 0.58) and used here for comparison.

C. H. Bocka, G. H. Pooleb, P. E. Parkerc, T. R. Gottwald, "Plant Disease Severity Estimated Visually by Digital Photography and Image Analysis and by Hyperspectral Imaging," Critical Reviews in Plant Science, vol. 29, no. 2, pp. 59-107, 2010. doi: 10.1080/07352681003617285.

J.G.A. Barbedo, “A Review on the Main Challenges in Automatic Plant Disease Identification Based on Visible Range Images,” Biosystems Engineering, vol. 144, pp. 52-60, April 2016. doi: 10.1016/j.biosystemseng.2016.01.017.

A. Clí©ment, T. Verfaille, C. Lormel, B. Jaloux, “A New Colour Vision System to Quantify Automatically Foliar Discolouration Caused by Insect Pests Feeding on Leaf Cells,” Biosystems Engineering, vol. 133, no. 0, pp. 128-140, 2015, ISSN 1537-5110. doi: 10.1016/j.biosystemseng.2015.03.007.

O. M. O. Kruse, J. M. Prats-MontalbíƒÂ¡n, U. G. Indahl, K. Kvaal, A. Ferrer, C. M. Futsaether, “Pixel Classification Methods for Identifying and Quantifying Leaf Surface Injury from Digital Images,” Computers and Electronics in Agriculture, vol. 108, pp. 155-165, 2014. ISSN 0168-1699, http://dx.doi.org/10.1016/j.compag.2014.07.010.

Pydipati, R., Burks T.F. and Lee, W.S., "Identification of Citrus Disease Using Color Texture Features and Discriminant Analysis," Computer and Electronics in Agriculture, pp.49-59, 2006.

R. Zhou, S. I. Kaneko, F. Tanaka, M. Kayamori, M. Shimizu, "Disease Detection of Cercospora Leaf Spot in Sugar Beet by Robust Template Matching," Computers and Electronics in Agriculture, vol. 108, pp. 58-70, 2014.

S. Phadikar, J. Sil, A. Kumar, "Rice Diseases Classification Using Feature Selection and Rule Generation Techniques,” Computer and Electronics in Agriculture, vol. 90, pp. 76-85, 2013. doi: 10.1016/j.compag.2012.11.001.

A. Camargo, J.S. Smith, "An Image-processing Based Algorithm to Automatically Identify Plant Disease Visual Symptoms,” Biosystems Engineering, vol. 102, no. 1, pp. 9-21, January 2009. ISSN 1537-5110. doi: 10.1016/j.biosystemseng.2008.09.030.

J.G.A Barbedo, “A New Automatic Method for Disease Symptom Segmentation in Digital Photographs of Plant Leaves,” European Journal of Plant Pathology, vol. 147, no 2, pp. 349-364, 2016. doi: 10.1007/s10658-016-1007-6.

D. P Hughes, M. Salathe, "An Open Access Repository of Images on Plant Health to Enable the Development of Mobile Disease Diagnostics,” CoRR, vol. abs/1511.08060, 2015.

A. Borji, M.-M. Cheng, H. Jiang, and J. Li, “Salient Object Detection: A Benchmark,” IEEE Trans. Image Process., vol. 24, no. 12, pp. 5706-5722, Dec. 2015.

D. Stutz, A. Hermans, B. Leibe, “Superpixels: An evaluation of the state-of-the-art”, Computer Vision and Image Understanding, Volume 166, 2018, Pages 1-27, ISSN 1077-3142.

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua and S. Sí¼sstrunk, "SLIC Superpixels Compared to State-of-the-Art Superpixel Methods," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2274-2282, Nov. 2012. doi: 10.1109/TPAMI.2012.120.

R. Hecht-Nielsen, "Theory of the Backpropagation Neural Network,” Proceedings of International Joint Conference on Neural Networks, vol. 1, pp. 593-605, 1989.

V.Garcia, H. de Jesus Ochoa Dominguez, B. Mederos. “Analysis of Discrepancy Metrics Used in Medical Image Segmentation.” IEEE Latin America Transactions, 13(1), (2015) 235-240.

C. Dharmagunawardhana, S. Mahmoodi, M. Bennett, M. Niranjan, “Gaussian Markov Random Field Based Improved Texture Descriptor for Image Segmentation,” Image and Vision Computing, vol. 32, issue 11, pp. 884-895, 2014. ISSN 0262-8856, http://dx.doi.org/10.1016/j.imavis.2014.07.002.

M. S. Laursen, H. S. Midtiby, N. Krí¼ger, and R. N. Jí¸rgensen, “Statistics-based Segmentation Using a Continuous-scale Naive Bayes Approach,” Computers and Electronics in Agriculture, vol. 109, pp. 271-277, 2014.4

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).