Tomato disease detection with lightweight recurrent and convolutional deep learning models for sustainable and smart agriculture
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Other Title
Authors
Le, An Thanh
Shakiba, Masoud
Ardekani, Iman
Shakiba, Masoud
Ardekani, Iman
Author ORCID Profiles (clickable)
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Grantor
Date
2024-06-20
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Type
Journal Article
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
tomato plants
leaf spots
image processing
neural networks
modelling
Internet of Things (IoT)
leaf spots
image processing
neural networks
modelling
Internet of Things (IoT)
ANZSRC Field of Research Code (2020)
Citation
Le, A. T., Shakiba, M., & Ardekani, I. T. (2024). Tomato disease detection with lightweight recurrent and convolutional deep learning models for sustainable and smart agriculture. Frontiers in Sustainability, 5, 1-6. https://doi.org/10.3389/frsus.2024.1383182
Abstract
The detection of plant diseases is a critical concern in agriculture, as it directly impacts crop health, yields, and food security (Fang and Ramasamy, 2015). Traditionally, this task has relied heavily on the observations of farmers and agricultural experts, which is fraught with many shortcomings, including human error and the inability to identify latent or early-stage infections. In response to these limitations, the scientific community has developed multiple innovative solutions. Among these, image classification techniques have gained widespread adoption due to their cost-efficiency (Chhillar et al., 2020) and the ability to enable real-time monitoring, allowing farmers to promptly detect diseases and take timely action (Chen et al., 2020). Additionally, these techniques are highly scalable, adaptable, non-invasive, and non-destructive and can be applied to different crops and disease scenarios (Ramcharan et al., 2017).
Publisher
Frontiers Media S.A.
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Link to ePress publication
DOI
https://doi.org/10.3389/frsus.2024.1383182
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CC BY Attribution 4.0 International