Citrus Diseases Recognition by Using CNN

Authors

  • Wala'a Nsaif Jasim 1Department of Pharmacognosy, College of Pharmacy, University of Basra, Iraq.
  • Sahera Abued Sead Almola Department of Computer Science, College of Computer Science and Information Technology, University of Basra, Iraq.
  • Mohammed H. Haloob Alabiech General Company of Electricity Energy Production –Southern Region, Ministry of Electricity, Basrah-Tawisa Crossing-Iraq.
  • Esraa Jasem Harfash Department of Computer Science, College of Computer Science and Information Technology, University of Basra, Iraq

DOI:

https://doi.org/10.31449/inf.v46i7.4284

Abstract

Pattern recognition is attracting the interest of researchers in the recently few years as a machine learning approaches due to its vast extending application areas. he application area includes communications, medicine, automations, data mining, military intelligence, document classification, bioinformatics, speech recognition and business. In this research convolutional neural networks (CNN) using for building system to recognize diseases that are happened in citrus. In this study presented dataset for seven classes of citrus diseases which contains 2450 images such as anthracnose, brown rot, citrus black spot, citrus canker, citrus scob, melanose and sooty mold citrus. The proposed system recognizes learned via CNN. The experimental result shows our model has ability to recognize citrus diseases with high and robustness accuracy. The study presented here gives 88% recognition of citrus diseases for the entire database.

Author Biographies

Sahera Abued Sead Almola, Department of Computer Science, College of Computer Science and Information Technology, University of Basra, Iraq.

Sahera Abued Sead holds her MSC. degree in pattern recognition from Computer Science from Basrah University, Iraq . She works as a Lecturer in Department of Information System,College of Computer Science and Information Technology, University of Basra, Iraq. Her is research interest is Pattern Recognition ,Image Processing , Encryption and Hiding Information.Email : sahera.sead@uobasrah.edu.iq

Mohammed H. Haloob Alabiech, General Company of Electricity Energy Production –Southern Region, Ministry of Electricity, Basrah-Tawisa Crossing-Iraq.

Mohammed H. Alabiech has completed his Bachelor of Computer Science from Basrah University in 2000 and Masters of Computer Science from Basrah University, Iraq in 2018, He works as a Chief programmer in Ministry of Electricty- General Company of Electrical Energy Production – South Region.  His research interest is information security and networks

Esraa Jasem Harfash, Department of Computer Science, College of Computer Science and Information Technology, University of Basra, Iraq

Esraa Jasem Harfash  holds her PHD. degree in Automatic speech recognition from Computer Science from Basrah University, Iraq . She is a professor in Department of Computer Science, College of Computer Science and Information Technology, University of Basra, Iraq since 1995. Her research interest pattern recognition , Automatic speech recognition, and Signal Processing.Email : esra.harfash@uobasrah.edu.iq

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Published

2022-11-04

How to Cite

Jasim, W. N., Almola, S. A. S., Alabiech, M. H. H., & Harfash, E. J. (2022). Citrus Diseases Recognition by Using CNN. Informatica, 46(7). https://doi.org/10.31449/inf.v46i7.4284

Issue

Vol. 46 No. 7 (2022): Online-only issue

Section

Online-only

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