YOLOv11-Based Deep Learning System for Accurate and Real-Time Tomato Disease Classification

Isaac Oluwafemi Elesemoyo, Emmanuel Adeniyi, Adedoyin Oyebade, Yusuf Faruk
Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, PMLR 319:118-130, 2026.

Abstract

This study proposes a system for detecting and classifying tomato diseases using the YOLOv11 model in real time. The proposed system utilised the PlantVillage dataset of 18,160 images across ten diseased and one healthy tomato class. With data processing, transfer learning, and hyperparameter optimisation, the trained YOLOv11 model achieved an accuracy of 99.2% and mean average precision (mAP@0.5) of 0.93. The system was deployed on a lightweight web application built with React.js and FastAPI, enabling users to upload images and receive instant predictions. The system has potential for reducing dependency on expert physical inspection and minimising yield losses in Nigerian agriculture.

Cite this Paper

BibTeX
@InProceedings{pmlr-v319-elesemoyo26a, title = {{YOLOv11}-Based Deep Learning System for Accurate and Real-Time Tomato Disease Classification}, author = {Elesemoyo, Isaac Oluwafemi and Adeniyi, Emmanuel and Oyebade, Adedoyin and Faruk, Yusuf}, booktitle = {Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments}, pages = {118--130}, year = {2026}, editor = {Folorunso, Sakinat and Ogundokun, Roseline and Oladipo, Francisca}, volume = {319}, series = {Proceedings of Machine Learning Research}, month = {11--14 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v319/main/assets/elesemoyo26a/elesemoyo26a.pdf}, url = {https://proceedings.mlr.press/v319/elesemoyo26a.html}, abstract = {This study proposes a system for detecting and classifying tomato diseases using the YOLOv11 model in real time. The proposed system utilised the PlantVillage dataset of 18,160 images across ten diseased and one healthy tomato class. With data processing, transfer learning, and hyperparameter optimisation, the trained YOLOv11 model achieved an accuracy of 99.2% and mean average precision (mAP@0.5) of 0.93. The system was deployed on a lightweight web application built with React.js and FastAPI, enabling users to upload images and receive instant predictions. The system has potential for reducing dependency on expert physical inspection and minimising yield losses in Nigerian agriculture.} }
Endnote
%0 Conference Paper %T YOLOv11-Based Deep Learning System for Accurate and Real-Time Tomato Disease Classification %A Isaac Oluwafemi Elesemoyo %A Emmanuel Adeniyi %A Adedoyin Oyebade %A Yusuf Faruk %B Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments %C Proceedings of Machine Learning Research %D 2026 %E Sakinat Folorunso %E Roseline Ogundokun %E Francisca Oladipo %F pmlr-v319-elesemoyo26a %I PMLR %P 118--130 %U https://proceedings.mlr.press/v319/elesemoyo26a.html %V 319 %X This study proposes a system for detecting and classifying tomato diseases using the YOLOv11 model in real time. The proposed system utilised the PlantVillage dataset of 18,160 images across ten diseased and one healthy tomato class. With data processing, transfer learning, and hyperparameter optimisation, the trained YOLOv11 model achieved an accuracy of 99.2% and mean average precision (mAP@0.5) of 0.93. The system was deployed on a lightweight web application built with React.js and FastAPI, enabling users to upload images and receive instant predictions. The system has potential for reducing dependency on expert physical inspection and minimising yield losses in Nigerian agriculture.
APA
Elesemoyo, I.O., Adeniyi, E., Oyebade, A. & Faruk, Y.. (2026). YOLOv11-Based Deep Learning System for Accurate and Real-Time Tomato Disease Classification. Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, in Proceedings of Machine Learning Research 319:118-130 Available from https://proceedings.mlr.press/v319/elesemoyo26a.html.

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