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--- |
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tags: |
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- image-classification |
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metrics: |
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- accuracy |
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license: mit |
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--- |
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# Grapevine Disease Classification Model |
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## Overview |
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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. |
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## Model Details |
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- **Model Architecture**: Convolutional Neural Network (CNN) |
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- **Input**: Images of grape leaves |
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- **Output**: Binary classification indicating whether the leaf is healthy or affected by Esca |
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- **focal_loss function**: |
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``` |
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def focal_loss(gamma=2., alpha=0.25): |
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def focal_loss_fixed(y_true, y_pred): |
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y_true = tf.cast(y_true, tf.float32) |
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y_pred = tf.clip_by_value(y_pred, tf.keras.backend.epsilon(), 1 - tf.keras.backend.epsilon()) |
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alpha_t = y_true * alpha + (tf.ones_like(y_true) - y_true) * (1 - alpha) |
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p_t = y_true * y_pred + (tf.ones_like(y_true) - y_true) * (tf.ones_like(y_true) - y_pred) |
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focal_loss_value = -alpha_t * tf.math.pow((tf.ones_like(y_true) - p_t), gamma) * tf.math.log(p_t) |
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return tf.reduce_mean(focal_loss_value) |
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return focal_loss_fixed |
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``` |
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## Dataset |
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The model was trained on a dataset of grapevine leaves collected from various vineyards. The dataset includes: |
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- **Healthy Leaves**: Images of grapevine leaves that are not affected by Esca disease but may contain other diseases. |
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- **Esca-Affected Leaves**: Images of grapevine leaves showing symptoms of Esca disease, such as discoloration, brown spots, and unusual texture. |
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### Data Source |
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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. |
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## Model Performance |
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### Evaluation Metrics |
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The model was evaluated using standard classification metrics, including precision, recall, and F1-score, for both classes (healthy and Esca-affected). |
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### Performance |
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precision recall f1-score support |
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esca 0.79 0.97 0.87 480 |
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healthy 0.99 0.90 0.94 1325 |
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- Accuracy: 0.92 |
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- Image AUC: 0.989 |
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### License |
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The data used to train this model is licensed under the CC0 Public Domain Dedication. The model itself is licensed under the MIT License. |
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### Acknowledgements |
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Special thanks to the contributors of the Grapevine Disease Dataset for providing the data used in training this model. |
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