Intelligent Crop Disease Detection System using Leaf image Analysis with Computer Vision

Authors

Lydie Simone Tapsoba, Mathematics and Informatics laboratory (LAMI), Burkina Faso

Wendolian Romain Soalla, Phytopathology Laboratory, Burkina Faso

Nebnoma Emmanuel Kiebre, Joseph KI-ZERBO University, Burkina Faso

Published in

IST-Africa 2026 Conference Proceedings

ISSN: 2576-8581

ISBN: 978-1-905824-76-2

DOI: https://doi.org/10.67725/IST-Africa.2026.SQQZ6055

Publisher

IST-Africa Institute and IIMC International Information Management Corporation Ltd

Published in Ireland

Abstract

Burkina Faso's agriculture sector faces major challenges, with annual crop losses reaching 40% due to plant diseases, affecting 2.7 million people in a situation of food insecurity. This research presents an innovative automatic plant disease detection system using computer vision, specifically developed for West African constraints. The system is based on the YOLOv11 (You Only Look Once) unified detection architecture, recognised for its optimal balance between speed and accuracy in real time, essential for mobile deployment to detect diseases in maize, tomatoes and chillies with an overall accuracy of ~99%. The image dataset from Kaggle has been validated by local agronomic expertise from INERA, ensuring the relevance of disease classes specific to the Sahelian context. The proposed architecture demonstrates superior performance to existing approaches while being optimised for mobile deployment. This solution contributes to the development of decision support tools for precision agriculture in West Africa.

Keywords

Deep Learning, YOLOv11, Disease Classification, Precision Agriculture, Burkina Faso, Agronomic Validation

Cite this paper

L.S. Tapsoba, W.R. Soalla and N.E. Kiebre (2026) "Intelligent Crop Disease Detection System using Leaf image Analysis with Computer Vision", IST-Africa 2026 Conference Proceedings, Miriam Cunningham and Paul Cunningham (Eds), IST-Africa Institute and IIMC, 2026, ISSN: 2576-8581, ISBN: 978-1-905824-76-2, https://doi.org/10.67725/IST-Africa.2026.SQQZ6055

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