Automated carrot disease recognition: a computer vision approach

  • Authors

    • Anup Majumder Daffodil International Universuty
    • Md. Tarek Habib Daffodil International Universuty
    • Papiya Hossain Lima Daffodil International Universuty
    • Saifuddin Sourav Daffodil International Universuty
    • Rabindra Nath Nandi Khulna University of Engineering and Technology
    2019-04-21
    https://doi.org/10.14419/ijet.v7i4.27019
  • Carrot Disease, Agro-Medical Expert System, Computer Vision, k-means Clustering, Support Vector Machine, Performance Metric.
  • To ensure the freshness of fruits and vegetables modern image processing tools can help a lot. Experts can detect the defected fruits and vegetables by watching them with their eyes but the process is too long and not suitable for all the stores, farms, supermarkets or the exporters all around. There comes the blessings of new computer vision technologies with image processing techniques that can do a lot of works in a second. In this paper an automated approach is developed to detect defects of fruits and vegetables and recognize diseases by using machine vision based image processing techniques. There are many algorithms that can detect defects of fruits and vegetables hence, we separated the defected parts of the carrots using k-means clustering and then classified it with Multiclass Support Vector Machine. Here, a supervised machine learning concept is implemented to recognize various carrot diseases. As the domain of this research model, carrot diseases are classified and 96% of accuracy is achieved which can certainly help in our agricultural science along with proper maintenance.

     

     
  • References

    1. [1] List of countries by population (United Nations) <<https://en.wikipedia.org/wiki/List_of_countries_by_population_(United_Nations)>> [Last Accessed on 13.07.2018 at 10:00pm.].

      [2] Importance of Agriculture in the Economy of Bangladesh <<http://www.sikkha.net/2016/02/importance-of-agriculture-in-economy-of.html>> [Last Accessed on 13.07.2018 at 10:00pm.].

      [3] http://www.infokosh.gov.bd/krishokerjanala/all/link/carrotlist.html [Last Accessed on 13.07.2018 at 10:00pm.].

      [4] M. T. Habib, A. Majumder, A. Z. M. Jakaria, M. Akter, Md. S. Uddin, F. Ahmed “Machine vision-based papaya disease recognitionâ€. Journal of King Saud University – Computer and Information Sciences (2018), https://doi.org/10.1016/j.jksuci.2018.06.006.

      [5] Howarth, M.S.; Searcy, S.W., 1989: Algorithms for grading carrots by machine vision. Paper American Society of Agricultural Engineers (89-7502): 17 pp.

      [6] Howarth, M.S.; Searcy, S.W., 1992 “Estimation of Tip Shape for Carrot Classification by Machine Visionâ€, J. ugric. Engng Res. (1992) 53, 123-139.https://doi.org/10.1016/0021-8634(92)80078-7.

      [7] Tao, Y.; Morrow, C. T.; Heinemann, P. H.; Sommer, J. H. “Automated machine vision inspection of potatoesâ€. American Society of Agricultural Engineers 1990 No.90-3531 pp.23 pp. ref.27.

      [8] F. López-García, G. Andreu-García, “Automatic detection of skin defects in citrus fruits using a multivariate image analysis approachâ€, Computers and Electronics in Agriculture, Volume 71, Issue 2, May 2010, Pages 189-197.https://doi.org/10.1016/j.compag.2010.02.001.

      [9] B. J. Samajpati, Sheshang D. Degadwala “Hybrid Approach for Apple Fruit Diseases Detection and Classification Using Random Forest Classifier†IEEE International Conference on Communication and Signal Processing, pp. 978-50900396, 2016.

      [10] A. S. Jalal, Shiv Ram Dubey “Detection and Classification of Apple Fruit Diseases Using Complete Local Binary Patterns†IEEE Third International Conference on Computer and Communication Technology, pp. 978-0-7695-4872, 2012.

      [11] Visual Detection of Blemishes in Potatoes using Minimalist Boosted Classiï¬ers†Michael Barnes1, Tom Duckett1, Grzegorz Cielniak1, Graeme Stroud2 and Glyn Harper2 1School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK 2Potato Council Ltd., Sutton Bridge Experimental Unit, Spalding PE12 9YD, UK.

      [12] Leemans, V., Magein, H., &Destain, M. F. “Defect Segmentation on Golden Delicious Apples by using Color Machine Vision†Computers and Electronics in Agriculture, 20, pp 117-130, 1998.https://doi.org/10.1016/S0168-1699(98)00012-X.

      [13] S. R. Dubey, Pushkar Dixit, Nishant Singh, Jay Prakash Gupta, “Infected Fruit Part Detection using k-means Clustering Segmentation Technique†International Journal of Artificial Intelligence and Interactive Multimedia, Vol. 2, Nº 2.

      [14] M. Jhuria, Ashwani Kumar, RushikeshBorse, “Image Processing for Smart Farming: Detection of Disease and Fruit Gradingâ€, IEEE Second International Conference on Image Information Processing, 2013, pp 521-526.

      [15] M. Bhange, H.A. Hingoliwala, “Smart Farming: Pomegranate Disease Detection Using Image Processing†– Procedia Computer Science 58 pp280-288, 2015.https://doi.org/10.1016/j.procs.2015.08.022.

      [16] B. K. Miller and M. J. Delwich“PEACH DEFECT DETECTION WITH MACHINE VISION†https://www.researchgate.net/publication/276014187_Peach_defect_detection_with_machine_vision, [Last Accessed on 13.07.2018 at 10:00pm.].

      [17] S. R. Dubey, “Automatic Recognition of Fruits and Vegetables and Detection of Fruit Diseases†<<https://www.researchgate.net/publication/317953072>> [Last Accessed on 13.07.2018 at 10:00pm.].

      [18] Gy. Dorko, D. Paulus, and U. Ahlrichs,†Color segmentation for scene exploration,†Workshop Farbbildverarbeitung, Oct 200, Berlin, Germany, 2000.

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    Majumder, A., Tarek Habib, M., Hossain Lima, P., Sourav, S., & Nath Nandi, R. (2019). Automated carrot disease recognition: a computer vision approach. International Journal of Engineering & Technology, 7(4), 5790-5797. https://doi.org/10.14419/ijet.v7i4.27019