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Classification of rice plant diseases using efficient DenseNet121.

Amr Ismail¹, Walid Hamdy^2,3, Ali H Ibrahim¹

¹Faculty of Science, Port Said University, Port Said, Egypt.

Scientific Reports

|February 20, 2026

Summary

This summary is machine-generated.

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.

Keywords:

CNN Classification DenseNet121 Machine learning Rice plant disease

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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.