Citation

Abbasi, Nergis Gulzar and Nazir, Aiman Khan and Ali, Sadia and Iqbal, Hamza and Ali, Atif and Shah, Asghar Ali and Abbas, Sagheer and Khan, Muhammad Adnan (2026) Web‐Based Sustainable Detection and Treatment Recommendation System for Wheat Plant Diseases Using Convolutional Neural Networks. Food Science & Nutrition, 14 (1). ISSN 2048-7177

Text Web‐Based Sustainable Detection and Treatment Recommendation System for Wheat Plant Diseases Using Convolutional Neural Networks.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

Wheat, being a major staple crop worldwide, is often attacked by rust diseases, which cause severe yield losses. The early detection and diagnosis of fungal infections, yellow rust, and brown rust are critical in minimizing their consequences. A web-based system based on a Convolutional Neural Network (CNN) was developed for the quick identification and classification of wheat plant diseases. The diseases that we examine in wheat plants are brown rust (BR) and yellow rust (YR), and healthy plants are classified in the third category. A dataset of labeled images of YR, BR, and healthy wheat plants was used to train the CNN. The model achieved a remarkable 96% classification accuracy. In addition to disease diagnosis, a recommendation module that gives advice on proper treatment based on disease names or symptoms is also provided. This twofold functionality allows for timely disease management and identification and facilitates the treatment of other wheat diseases besides rust diseases. Integrating the trained CNN model into an intuitive web application makes it user-friendly for end users, notably farmers, to have a practical tool in protecting wheat crops.

Item Type:	Article
Uncontrolled Keywords:	Convolutional neural networks
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Others
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	11 Feb 2026 02:42
Last Modified:	11 Feb 2026 02:42
URII:	http://shdl.mmu.edu.my/id/eprint/15355

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