Plant disease detection model for edge computing devices
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In this paper, we address the question of achieving high accuracy in deep learning models for agricultural applications through edge computing devices while considering the associated resource constraints. Traditional and state-of-the-art models have demonstrated good accuracy, but their practicality as end-user available solutions remains uncertain due to current resource limitations. One agricultural application for deep learning models is the detection and classification of plant diseases through image-based crop monitoring. We used the publicly available PlantVillage dataset containing images of healthy and diseased leaves for 14 crop species and 6 groups of diseases as example data. The MobileNetV3-small model succeeds in classifying the leaves with a test accuracy of around 99.50%. Post-training optimization using quantization reduced the number of model parameters from approximately 1.5 million to 0.93 million while maintaining the accuracy of 99.50%. The final model is in ONNX format, enabling deployment across various platforms, including mobile devices. These findings offer a cost-effective solution for deploying accurate deep-learning models in agricultural applications.
Originalsprog | Engelsk |
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Artikelnummer | 1308528 |
Tidsskrift | Frontiers in Plant Science |
Vol/bind | 14 |
Antal sider | 10 |
ISSN | 1664-462X |
DOI | |
Status | Udgivet - 2023 |
Bibliografisk note
Funding Information:
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
Publisher Copyright:
Copyright © 2023 Khan, Jensen, Khan and Li.
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