ARCHIVES
VOL. 12, ISSUE 2 (2026)
Identification of plant diseases using deep learning
Authors
Ibrahim Aliyu Yabo, Hudu Burah, Mohammed Adamu Sule, Kadawa Ibrahim Ali, Sabiru Sarki Tudu, Abubakar Abubakar Aliyu
Abstract
The agricultural sector plays a vital role in
the economy of country. Agricultural output is very vital in many developing
countries. Increase in population and increase in the life expectancy is
pressurizing the agricultural sector to come out with new types of high
yielding crops. The diseases in the plants are common, early detection and
control increases the yield of a crop. The problems of leaf in plants are often
dangerous and they usually shorten the lifespan of plants. Leaf diseases are
mainly caused by three types of attacks including viral, bacterial or fungal.
Diseased leaves reduce the crop production and affect the agricultural economy.
Since agriculture plays a vital role in the economy, thus effective mechanism
is required to detect the problem in early stages. This research developed a
scheme for identification of plant diseases using deep learning which utilizes
transfer learning using pre-trained Convolutional Neural Network (CNN). The
developed scheme uses images obtained from developed dataset of two different
plant leaves images from KSUSTA, called the KSUSTA dataset. Denoising,
downscaling operation and RGB conversion applied on the acquired image to
reduce the cost incurred in using the original image. The developed scheme was
based on current image processing and computer vision techniques, to accurately
detect and identify the healthy and non-healthy plant leaves from a given image
of the plant leaf with less false positive prediction. The experimental result
shows that the developed scheme outperformed the existing one with a large
margin.
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Pages:11-16
How to cite this article:
Ibrahim Aliyu Yabo, Hudu Burah, Mohammed Adamu Sule, Kadawa Ibrahim Ali, Sabiru Sarki Tudu, Abubakar Abubakar Aliyu "Identification of plant diseases using deep learning". International Journal of Research in Advanced Engineering and Technology, Vol 12, Issue 2, 2026, Pages 11-16
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