Classification of healthy and diseased plant leaves using deep learning model
Abstract— In today's world, the combination of climate change, sustainable agriculture, and globalization highlights the vital need for plant disease preventive techniques. The connection between plant disease control and agriculture is discussed in this paper. It highlights how crucial ear...
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| Main Authors: | , , , , , |
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| Format: | Proceeding Paper |
| Language: | en |
| Published: |
IEEE
2025
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/123199/2/123199_Classification%20of%20Healthy.pdf http://irep.iium.edu.my/123199/ https://ieeexplore.ieee.org/abstract/document/11119833 |
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| Summary: | Abstract— In today's world, the combination of climate change,
sustainable agriculture, and globalization highlights the vital
need for plant disease preventive techniques. The connection
between plant disease control and agriculture is discussed in this
paper. It highlights how crucial early disease detection is
becoming and how deep learning technology can be used to
identify plant diseases. The methodology focuses on identifying
plant species and detecting the health conditions of plants by
utilizing deep learning techniques for plant disease
classification. Moreover, The following procedures must be
followed in order to use deep learning techniques to discriminate
between healthy and sick plants: data collection, data
preprocessing, model selection, model training, model
assessment, and deployment. In this paper, the authors assess
four primary metrics—accuracy, recall, precision, and F1-
score—for three distinct models: ResNet50, MobileNetV3, and
EfficientNet. In experimentation work, the authors have
analyzed model performance and classification accuracy, with
EfficientNetB5 proving to be the most accurate method having
98.52% accuracy. This categorization has great promise for
transforming farming methods and guaranteeing food security
for all people on the planet. |
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