Wu, Jiayi and Abolghasemi, Vahid and Anisi, Mohammad Hossein and Dar, Usman and Ivanov, Andrey and Newenham, Chris (2024) Strawberry Disease Detection through an Advanced Squeeze-and-Excitation Deep Learning Model. IEEE Transactions on AgriFood Electronics, 2 (2). pp. 259-267. DOI https://doi.org/10.1109/TAFE.2024.3412285
Wu, Jiayi and Abolghasemi, Vahid and Anisi, Mohammad Hossein and Dar, Usman and Ivanov, Andrey and Newenham, Chris (2024) Strawberry Disease Detection through an Advanced Squeeze-and-Excitation Deep Learning Model. IEEE Transactions on AgriFood Electronics, 2 (2). pp. 259-267. DOI https://doi.org/10.1109/TAFE.2024.3412285
Wu, Jiayi and Abolghasemi, Vahid and Anisi, Mohammad Hossein and Dar, Usman and Ivanov, Andrey and Newenham, Chris (2024) Strawberry Disease Detection through an Advanced Squeeze-and-Excitation Deep Learning Model. IEEE Transactions on AgriFood Electronics, 2 (2). pp. 259-267. DOI https://doi.org/10.1109/TAFE.2024.3412285
Abstract
In this article, an innovative deep learning-driven framework, adapted for the identification of diseases in strawberry plants, is proposed. Our approach encompasses a comprehensive embedded electronic system, incorporating sensor data acquisition and image capturing from the plants. These images are seamlessly transmitted to the cloud through a dedicated gateway for subsequent analysis. The research introduces a novel model, ResNet9-SE, a modified ResNet architecture featuring two squeeze-and-excitation (SE) blocks strategically positioned within the network to enhance performance. The key advantage gained is achieving fewer parameters and occupying less memory while preserving a high diagnosis accuracy. The proposed model is evaluated using in-house collected data and a publicly available dataset. The experimental outcomes demonstrate the exceptional classification accuracy of the ResNet9-SE model (99.7%), coupled with significantly reduced computation costs, affirming its suitability for deployment in embedded systems.
Item Type: | Article |
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Uncontrolled Keywords: | Computer vision; crop monitoring; deep learning; Internet of Things (IoT); plant disease detection |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 08 Jul 2024 14:52 |
Last Modified: | 25 Oct 2024 01:08 |
URI: | http://repository.essex.ac.uk/id/eprint/38509 |
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