| Year |
2024
50 Downloads
|
| Volume/Issue/Review Month |
Volume 1 | Special Issue | October |
| Title |
A Deep Learning Approach for Wheat Rust Disease Classification |
| Authors |
Ch Biswaranjan Nanda , Sudhir Kumar Mohapatra , Rabi Narayan Satpathy |
| Broad area |
A Deep Learning Approach for Wheat Rust Disease Classification |
| Abstract |
Wheat rust is a devastating fungal disease that affects wheat crops worldwide, leading
to significant yield losses and economic damage. Early detection and accurate classification of
wheat rust diseases are crucial for effective disease management strategies. In this study, we propose
a deep learning-based approach for wheat rust disease classification. Our model utilizes a
convolutional neural network (CNN) architecture trained on a large dataset of wheat rust images.
We employ data augmentation techniques to enhance the model’s ability to generalize and perform
well on unseen data. Through rigorous experimentation and hyperparameter tuning, we achieve
remarkable results with a training accuracy of 96% and a testing accuracy of 94%.The high
accuracy rates demonstrate the effectiveness of our model in accurately classifying wheat rust
diseases, which is essential for timely intervention and mitigation efforts. This research contributes
to the advancement of automated disease detection systems in agriculture, paving the way for
improved crop management practices and enhanced food security. |
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