Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper


March Edition 2023

International Journal of Applied Information Systems solicits high quality original research papers for the March 2023 Edition of the journal. The last date of research paper submission is February 15, 2023.

Measuring the Severity of Fungi Caused Disease on Leaves using Triangular Thresholding Method

Dominic Asamoah, Richard Marfo, Stephen Opoku Oppong. Published in Image Pprocessing

International Journal of Applied Information Systems
Year of Publication: 2017
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Dominic Asamoah, Richard Marfo, Stephen Opoku Oppong
Download full text
  1. Dominic Asamoah, Richard Marfo and Stephen Opoku Oppong. Measuring the Severity of Fungi Caused Disease on Leaves using Triangular Thresholding Method. International Journal of Applied Information Systems 12(1):24-32, April 2017. URL, DOI BibTeX

    	author = "Dominic Asamoah and Richard Marfo and Stephen Opoku Oppong",
    	title = "Measuring the Severity of Fungi Caused Disease on Leaves using Triangular Thresholding Method",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "April 2017",
    	volume = 12,
    	number = 1,
    	month = "Apr",
    	year = 2017,
    	issn = "2249-0868",
    	pages = "24-32",
    	url = "",
    	doi = "10.5120/ijais2017451668",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"


Leaf disease detection and measurement is one of the most difficult tasks in agricultural image processing. This study discus in details the methods and means of detecting and measuring the severity of fungi caused disease on plant leaves using the triangular thresholding method. Four suspected images ware collected from different plant species and experiments were conducted on each to detect and measure the extent of damage caused by the fungi cause disease on the leaf. Analysis was made and the results proved to be about 97% accurate.


  1. Rao M. 2004, A Novel technique to Resample High resolution remote Sensing Satellite Images, Proc. Ofigrass-02, Colorado
  2. Jamayala B., Nikola Kasabov K. and Howard C. [1999] Fruit Image Analysis using Wavelets, Proceedings of the ICONIP/ANZIIS/ANNES.
  3. Libo L., Zhou G. M., 2009, "Research on image feature extraction of crop disease," Transactions of the CSAE, vol.2S, pp.213-217
  4. Zhang Y., Li M.Z. 2005, "Nutrition information extraction of the cucumber leaves in the greenhouse based on computer vision technology," Transactions of the CSAE, vo1.21, pp.102-IOS
  5. Hu C, Li P.P, 2004," Application of computer image processing to extract color feature of nutrient deficiency leaves,” Computer Measurement & Control, vol.9, pp.8S9-862
  6. Revathi P., Hemalatha M., 2012 “Advance Computing Enrichment Evaluation of Cotton Leaf Spot Disease Detection Using Image Edge detection”, IEEE-20180, ICCCNT'12, Coimbatore, India.
  7. Kim D., Burks T.F., Qin J., Bulanon D. 2009, Classification of grapefruit peel diseases using color texture feature analysis, International Journal on Agriculture and Biological Engineering, Vol:2, No:
  8. Helly M., Rafea A., and El-Gamma. An Integrated image Processing System for Leaf Disease Detection and Diagnosis
  9. Al-Bashish D., Braik M. and Bani-Ahmad S., 2011. Detection and classification of leaf diseases using K-means-based segmentation and neural networks based classification
  10. Sabine D. B., Forstne W., 2011, The Potential of Automatic Methods of Classification to identify Leaf diseases from Multispectral images, Springer Science Business Media, LLC 2011., Precision Agric (2011) 12:361-377, DOI 10.1007/s11119-011-9217-6.
  11. Husin Z., Farook M.S, 2012,” Feasibility Study on Plant Chili Disease Detection Using Image Processing Techniques” in Proceedings of IEEE International Conference on Intelligent Systems Modelling and Simulation.
  12. Song Y., Diao Z., Wang Y., Wang H. 2012, “Image Feature Extraction of Crop Disease”, in IEEE Symposium on Electrical& Electronics Engineering (EEESYM)


Segmentation, Thresholding, Image Acquisition, Triangular Thresholding, Leaf Disease