| # Grapevine Disease Classification Model | |
| ## Overview | |
| This model is designed to classify grapevine leaves as either "healthy" or affected by [Esca](https://ipm.ucanr.edu/agriculture/grape/esca-black-measles/#gsc.tab=0) disease. For this model, healthy is defined as not having signs of Esca, meaning signs of blight, rot, and other infections will be classified as healthy/non-Esca. Esca is a serious fungal disease that affects grapevines, causing significant damage to vineyards. Early detection of Esca can help in managing and controlling its spread, ensuring healthier vineyards and better grape yields. | |
| ## Model Details | |
| - **Model Architecture**: Convolutional Neural Network (CNN) | |
| - **Input**: Images of grape leaves | |
| - **Output**: Binary classification indicating whether the leaf is healthy or affected by Esca | |
| ## Dataset | |
| The model was trained on a dataset of grapevine leaves collected from various vineyards. The dataset includes: | |
| - **Healthy Leaves**: Images of grapevine leaves that are not affected by Esca disease but may contain other diseases. | |
| - **Esca-Affected Leaves**: Images of grapevine leaves showing symptoms of Esca disease, such as discoloration, brown spots, and unusual texture. | |
| ### Data Source | |
| The dataset used to train this model is sourced from the [Grapevine Disease Dataset](https://www.kaggle.com/datasets/rm1000/grape-disease-dataset-original?resource=download) available under the CC0 Public Domain Dedication. | |
| ## Model Performance | |
| ### Evaluation Metrics | |
| The model was evaluated using standard classification metrics, including precision, recall, and F1-score, for both classes (healthy and Esca-affected). | |
| ### Classification Report | |
| precision recall f1-score support | |
| esca 0.79 0.97 0.87 480 | |
| healthy 0.99 0.90 0.94 1325 | |
| ### Accuracy | |
| - Accuracy: 0.92 | |
| ### Confusion Matrix | |
| - **True Positives (TP)**: `esca` correctly identified as `esca`: 468 | |
| - **True Negatives (TN)**: `healthy` correctly identified as `healthy`: 1197 | |
| - **False Positives (FP)**: `healthy` incorrectly identified as `esca`: 12 | |
| - **False Negatives (FN)**: `esca` incorrectly identified as `healthy`: 128 | |
| ### License | |
| The data used to train this model is licensed under the CC0 Public Domain Dedication. The model itself is licensed under the MIT License. | |
| ### Acknowledgements | |
| Special thanks to the contributors of the Grapevine Disease Dataset for providing the data used in training this model. | |
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| tags: | |
| - image-classification | |
| metrics: | |
| - accuracy | |
| license: mit | |
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