Detection of Maize Streak Virus using Raspberry Pi.

Editor In Chief:

Professor Dr. G. Hussein Rassool


Dr. Muhammad Salama

Dr. Francesca Bocca

Dr. Nissar Yatoo 

Statistical Editor:

Mansoor Danish

Assistant Editors:

Veronika Matulova

Sumayyah Meehan

Associate Editor:

Aisha Nasim

Research Administrator:

Yasmin Toor

Social Media & Marketing:

Ayesha Shaukat

Mudassira Shafi

Recent Articles

Detection of Maize Streak Virus using Raspberry Pi.


Maize is one of the most common food crops grown annually around the world whereby the grains are further processed and used for local foods, manufacturing of cereals, animal feeds and many others. As a common food crop some challenges such as virus attacks are faced by farmers in the plant growth process which can result to poor grain yield on harvesting. In this paper, we present a novel algorithm for detecting a common virus known as maize streak virus (MSV). The proposed algorithm uses an image processing technique to detect the presence of MSV on maize leaves. Therefore, MSV is detected by capturing the images of maize leaves and then sending them to a Raspberry Pi computer which runs an image processing algorithm to determine if the maize plant is infected with the MSV.



Barbedo, J. G. A. (2013). Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus, 2(1), 660.
Maksimović, M., Vujović, V., Davidović, N., Milošević, V., & Perišić, B. (2014). Raspberry Pi as Internet of things hardware: performances and constraints. design issues, 3, 8.
Marathe, H. D., & Kothe, P. N. (2013). Leaf Disease Detection Using Image Processing Techniques. International Journal of Engineering Research & Technology (IJERT), 2(3), 2278-0181.
Martin, D. P., & Rybicki, E. P. (1998). Microcomputer- based quantification of maize streak virus symptoms in Zea mays. Phytopathology, 88(5), 422-427.
Patil, J. K., & Kumar, R. (2011). Advances in image processing for detection of plant diseases. Journal of Advanced Bioinformatics Applications and Research, 2(2), 135-141.
Pesco, D. U., & Bortolossi, H. J. Matrices and Digital Images.
Sahoo, P. K., Soltani, S., & Wong, A. K. (1988). A survey of thresholding techniques. Computer vision, graphics, and image processing, 41(2), 233-260.
Sasakawa, K., Kuroda, S. i., & Ikebata, S. (1991). A method for threshold selection in binary images using mean adjacent‐pixel number. Systems and Computers in Japan, 22(3), 66-73.
Sethupathy, J., & Veni, S. (2016). OpenCV Based Disease Identification of Mango Leaves. International Journal of Engineering and Technology, 8(5), 1990-1998.
van Regenmortel, M. H., & Mahy, B. W. (2009). Desk encyclopedia of plant and fungal virology: Academic Press.
Young, I. T., Gerbrands, J. J., & Van Vliet, L. J. (1998). Fundamentals of image processing: Delft University of Technology Delft.