Mobile-Optimised Deep Learning Architecture for Multi-Crop Disease Detection Using CNN and SVM

Abstract:

In this paper, we propose a deep learning pipeline for real-time crop disease classification on mobile devices. Our system employs a custom Convolutional Neural Network (CNN) trained on publicly available crop disease datasets (Maize, Tomato, Potato, Rice). In addition, two transfer-learning models; ResNet-50 and MobileNet are used as fixed feature extractors, with their output features classified by a multi-class Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. We compare the models’ performance across all crop datasets and evaluate inference latency and model size. Experimental results show that the ResNet50-SVM hybrid attains near-perfect accuracy (≈100% for Maize, Tomato, Potato; 99.96% for Rice) on plant disease classification, far exceeding both the custom CNN and MobileNet-SVM approaches. The MobileNet-SVM pipeline is notably faster (≈23–66 ms per image) and compact (~8.7 MB) than ResNet50+SVM (≈108–192 ms, ~90 MB), making it well-suited for on-device deployment. The final model is converted to TensorFlow Lite for mobile inference; on a typical smartphone CPU it processes an input image in ~0.15–0.19s on average, enabling practical field use. These results demonstrate an efficient mobile AI solution for crop disease detection that balances accuracy with resource constraints. The proposed system can empower farmers with timely, in-field disease diagnosis, helping to mitigate yield losses and improve crop management through accessible AI-driven tools.

• Periodical: Engineering Headway (Volume 37)
• Pages: 87-97
• DOI: https://doi.org/10.4028/p-HANB9o
• Online since: March 2026
• Authors: Yusuf Ibrahim*, Muhammed Bashir Mu'azu, Mustapha Abdullahi Jimoh, Abdullahi Yunus
• Keywords: Convolutional Neural Network (CNN), Crop Disease Detection, Mobile AI, MobileNet, ResNet, Support Vector Machine (SVM), TensorFlow Lite

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