Plants Monitoring API to Detect Tomato Leaf Diseases using Deep-Learning Algorithms

Moustafa, Ayman and Alsewari, AbdulRahman and Hassan, Sara (2024) Plants Monitoring API to Detect Tomato Leaf Diseases using Deep-Learning Algorithms. In: 7th International Conference of Reliable Information and Communication Technology (IRICT 2023), 27th - 28th December 2023, Johor, Malaysia. (In Press)

Text IRICT_2023_Camera_Ready_Paper_66.pdf - Accepted Version Restricted to Repository staff only Download (439kB)

Abstract

In the presented study, the challenge of detecting tomato leaf diseases, crucial for sustainable agriculture, is addressed. A Convolutional Neural Net-work (CNN) model has been developed, demonstrating a high accuracy rate of 97.29% on a variety of test datasets. The methodology employed includes a comprehensive literature review, meticulous collection and augmentation of datasets, and the development of an advanced CNN model. Through these techniques, early and precise detection of diseases in tomato plants is facilitated. The contributions of this research are pivotal in transforming agricultural practices, as evidenced by enhanced crop health, increased yield, and promotion of sustainable farming methods. The significance of this work lies in its potential to contribute to global food security and the evolution of agriculture in the digital era.

Item Type:	Conference or Workshop Item (Paper)
Dates:	Date Event 7 January 2024 Accepted
Uncontrolled Keywords:	CNN, Machine learning, Deep learning, Tomato disease detection
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User:	Gemma Tonks
Date Deposited:	19 Feb 2024 12:09
Last Modified:	19 Feb 2024 12:09
URI:	https://www.open-access.bcu.ac.uk/id/eprint/15264

Actions (login required)

View Item