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Rice Leaves Disease Diagnose Empowered with Transfer Learning

Nouh Sabri Elmitwally1,2, Maria Tariq3,4, Muhammad Adnan Khan5,*, Munir Ahmad3, Sagheer Abbas3, Fahad Mazaed Alotaibi6

1 College of Computer and Information Sciences, Jouf University, Sakaka, 72341, Saudi Arabia
2 Department of Computer Science, Faculty of Computers and Artificial Intelligence, Cairo University, 12613, Egypt
3 School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
4 Department of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan
5 Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam, 13557, Korea
6 Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

* Corresponding Author: Muhammad Adnan Khan. Email: email

Computer Systems Science and Engineering 2022, 42(3), 1001-1014.


In the agricultural industry, rice infections have resulted in significant productivity and economic losses. The infections must be recognized early on to regulate and mitigate the effects of the attacks. Early diagnosis of disease severity effects or incidence can preserve production from quantitative and qualitative losses, reduce pesticide use, and boost ta country’s economy. Assessing the health of a rice plant through its leaves is usually done as a manual ocular exercise. In this manuscript, three rice plant diseases: Bacterial leaf blight, Brown spot, and Leaf smut, were identified using the Alexnet Model. Our research shows that any reduction in rice plants will have a significant beneficial impact on alleviating global food hunger by increasing supply, lowering prices, and reducing production's environmental impact that affects the economy of any country. Farmers would be able to get more exact and faster results with this technology, allowing them to administer the most acceptable treatment available. By Using Alex Net, the proposed approach achieved a 99.0% accuracy rate for diagnosing rice leaves disease.


Cite This Article

N. Sabri Elmitwally, M. Tariq, M. Adnan Khan, M. Ahmad, S. Abbas et al., "Rice leaves disease diagnose empowered with transfer learning," Computer Systems Science and Engineering, vol. 42, no.3, pp. 1001–1014, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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