Maize Plant Disease Detection Using Convolutional Neural Network

Samkeliso Suku Dube and Heather Mambudzi
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Samkeliso Suku Dube: Computer Science Department, National University of Science and Technology Bulawayo, Zimbabwe
Heather Mambudzi: Computer Science Department, National University of Science and Technology Bulawayo, Zimbabwe

International Journal of Research and Innovation in Applied Science, 2025, vol. 10, issue 7, 157-166

Abstract: Convolutional Neural Networks (CNNs) have become a cornerstone of computer vision, particularly in image classification tasks. In agriculture, early identification of crop diseases like those affecting maize such as northern leaf blight, common rust, and gray leaf spot is important in order to reduce yield losses and ensure food security. However, accurate diagnosis remains a challenge for farmers without specialised expertise. This research leverages the power of CNNs and mobile technology to develop a mobile application capable of classifying maize diseases from leaf images. The system uses a CNN algorithm to classify the maize leaf diseases. The CNN model was trained on a dataset using a Google Collaboration framework, and it was integrated into an application built with React Native framework. To enhance accessibility for diverse farming communities, the application provides multilingual support, displaying disease symptoms and treatment recommendations in the user’s preferred language. By combining deep learning with mobile accessibility, this system aims to empower farmers with instant, actionable insights to safeguard their crops. The CNN model was trained and integrated into a React Native mobile application, achieving 88% validation accuracy whilst the model achieved an accuracy of 92.4%.

Date: 2025
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