Potato Plant Leaf Disease Diagnosis System
Student: Ezra Andrew (Project, 2025)
Department of Computer Science and Informatics
Kaduna State University, Kaduna, Kaduna State
Abstract
Potatoes are one of the world’s most important food crops, but they are vulnerable to various diseases that can reduce yield and quality. Many farmers in Nigeria still rely on manual inspection and random chemical application, which are often ineffective. This study introduces a Potato Plant Leaf Disease Diagnosis System that uses deep learning to improve disease detection. The system is powered by the EfficientNetB7 Convolutional Neural Network (CNN), a highly accurate model for image classification. The model was trained on a dataset of potato leaf images and achieved 98% accuracy on the test set and 97.5% validation accuracy, proving its reliability. The system is available as a web application, allowing farmers to upload or capture leaf images, get a diagnosis, and receive treatment advice. By providing a fast and accurate diagnosis, the system helps farmers make better decisions, improve crop health, and reduce losses.
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For the full publication, please contact the author directly at: ezraandrew@gmail.com
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Institutions
- Isa Mustapha Agwai I Polytechnic, Lafia, Nasarawa State 2
- Jigawa State Polytechnic, Dutse, Jigawa State 4
- Joseph Sarwuan Tarka University, Makurdi, Benue State 17
- Kaduna Polytechnic (NCE), Kaduna, Kaduna State 2
- Kaduna Polytechnic, Kaduna 328
- Kaduna Polytechnic, Kaduna , Kaduna State (affl To Fed Univ of Tech, Minna) 6
- Kaduna State College of Education, Gidan-Waya (affliatted To Abu) 2
- Kaduna State University, Kaduna, Kaduna State 246
- Kano State Polytechnic, Kano, Kano State 196
- Kano University of Science and Technology, Wudil, Kano State 6