Grape Guard: A YOLO-based mobile application for detecting grape leaf diseases

Sajib Bin Mamun; Israt Jahan Payel; Md Taimur Ahad; Anthony S. Atkins; Bo Song; Yan Li

doi:10.1016/j.jnlest.2025.100300

Journal of Electronic Science and Technology ›› 2025, Vol. 23 ›› Issue (1) :100300 DOI: 10.1016/j.jnlest.2025.100300

research-article

Grape Guard: A YOLO-based mobile application for detecting grape leaf diseases

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^a Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1207, Bangladesh

^b Department of Computer Science, University of Southern Queensland, Toowoomba, 4350, Australia

^c Faculty of Digital, Technology, Innovation, and Business, Staffordshire University, Stoke-on-Trent, ST4 2DE, UK

^d School of Engineering, University of Southern Queensland, Toowoomba, 4350, Australia

^e School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, 4350, Australia

^* E-mail addresses: Sajib15-3435@diu.edu.bd (S.B. Mamun),

israt15-3138@diu.edu.bd (I.J. Payel),

MdTaimur.Ahad@unisq.edu.au (M.T. Ahad),

a.s.atkins@staffs.ac.uk (A.S. Atkins),

Bo.Song@usq.edu.au (B. Song),

Yan.Li@usq.edu.au (Y. Li).

Sajib Bin Mamun was born in Dhaka, Bangladesh in 2000. He received his B.Sc. degree in computer science and engineering from Daffodil International University (DIU), Dhaka, Bangladesh in 2024. He is currently working as a research assistant at the 4IR Research Lab in DIU. His research interests include computer vision, image processing, feature extraction, signal processing, machine learning-based mobile edge device implementation, and information theory.

Israt Jahan Payel completed her B.Sc. degree in computer science and engineering from DIU. She is currently working as a research assistant at the 4IR Research Cell in DIU. Her research interests include image processing, computer vision, artificial intelligence, object detection, feature extraction, signal processing, and applications of machine learning and deep learning.

Md. Taimur Ahad received his Ph. D. in Computer Science from Macquarie University, Sydney, Australia. He is currently an Associate Professor with DIU. His research interests include utilizing the affordances of computer science to empower people who are often overlooked, paving the way for a more equitable world, accelerating healthcare progress, and increasing business agility using digital technology.

Anthony S. Atkins is a professor emeritus in digital transformation and AI with the Faculty of Digital Technology, Innovation, and Business, Staffordshire University. He is qualified in both mining engineering and computer science with 40 years of industrial and academic experience. He has published over 280 refereed publications consisting of 55 journals, 19 chapters in books, and conferences with colleagues and research students. His research fields include health informatics, artificial intelligence (AI), big data analytics, gamification, business intelligence, knowledge management, and applied computing.

Bo Song is a Lecturer in Industrial Automation at the University of Southern Queensland (USQ), Toowoomba, Australia. His research interest lies in multi-modal realistic human head modelling, image and signal processing, AI, and machine learning.

Yan Li is a Professor at USQ. Her research interests lie in the areas of AI, Machine Learning, Big Data and Internet Technologies, Security, Signal/Image Processing, and EEG Research. Prof Yan Li has published over 200 high-quality publications and supervised a significant number of Ph.D. completions.

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Abstract

Grape crops are a great source of income for farmers. The yield and quality of grapes can be improved by preventing and treating diseases. The farmer's yield will be dramatically impacted if diseases are found on grape leaves. Automatic detection can reduce the chances of leaf diseases affecting other healthy plants. Several studies have been conducted to detect grape leaf diseases, but most fail to engage with end users and integrate the model with real-time mobile applications. This study developed a mobile-based grape leaf disease detection (GLDD) application to identify infected leaves, Grape Guard, based on a TensorFlow Lite (TFLite) model generated from the You Only Look Once (YOLO)v8 model. A public grape leaf disease dataset containing four classes was used to train the model. The results of this study were relied on the YOLO architecture, specifically YOLOv5 and YOLOv8. After extensive experiments with different image sizes, YOLOv8 performed better than YOLOv5. YOLOv8 achieved 99.9 % precision, 100 % recall, 99.5 % mean average precision (mAP), and 88 % mAP50–95 for all classes to detect grape leaf diseases. The Grape Guard android mobile application can accurately detect the grape leaf disease by capturing images from grape vines.

Keywords

Bacterial diseases / Grape guard / Mobile-based application / YOLOv5 / YOLOv8

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Sajib Bin Mamun, Israt Jahan Payel, Md Taimur Ahad, Anthony S. Atkins, Bo Song, Yan Li. Grape Guard: A YOLO-based mobile application for detecting grape leaf diseases. Journal of Electronic Science and Technology, 2025, 23(1): 100300 DOI:10.1016/j.jnlest.2025.100300

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CRediT authorship contribution statement

Sajib Bin Mamun: Writing – original draft, Visualization, Data Curation, Methodology, Formal Analysis, Investigation, Conceptualization. Israt Jahan Payel: Writing – original draft, Data curation, Resources, Conceptualization. Md. Taimur Ahad: Writing – review & editing, Visualization, Validation, Formal Analysis, Supervision. Anthony S. Atkins: Writing - review & editing, validation. Bo Song: Writing – reviewing & editing. Yan Li: Writing – review & editing, supervision.

Declaration of competing interest

The authors declare no conflicts of interest.

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