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Classification of rice plant diseases using efficient DenseNet121
Amr Ismail1, Walid Hamdy2,3, Ali H Ibrahim1
1Faculty of Science, Port Said University, Port Said, Egypt.
Scientific Reports
|February 20, 2026
Summary
This study introduces DenseNet121 for accurate rice disease identification, achieving 97.9% accuracy. This AI-driven approach enhances crop monitoring and supports global food security.
Area of Science:
- Agricultural Science
- Computer Science
- Artificial Intelligence
Background:
- Accurate plant disease identification is crucial for agriculture and food security.
- Traditional methods lack speed and scalability.
- Deep learning offers automated solutions for plant disease diagnosis.
Purpose of the Study:
- To develop a comprehensive rice disease classification model.
- To address limitations of previous studies focusing on fewer disease classes.
- To implement the DenseNet121 architecture for improved accuracy.
Main Methods:
- Utilized a dataset of seven common rice diseases.
- Employed DenseNet121 with transfer learning from ImageNet weights.
- Optimized the model using the Adam optimizer with tuned hyperparameters.
Main Results:
- Achieved an overall accuracy of 97.9% on an independent test set.
- Individual disease classification accuracy ranged from 94% to 99.67%.
- Demonstrated robust performance with precision (96.2%), recall (97.97%), and F1-score (97%).
Conclusions:
- DenseNet121 is a highly effective framework for automated rice disease diagnosis.
- The model offers a practical tool for enhancing agricultural productivity.
- This AI approach supports sustainable agriculture and food security.
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