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---
tags:
- image-classification
metrics:
- accuracy
license: mit
---
# 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
- **focal_loss function**:
```
def focal_loss(gamma=2., alpha=0.25):
def focal_loss_fixed(y_true, y_pred):
y_true = tf.cast(y_true, tf.float32)
y_pred = tf.clip_by_value(y_pred, tf.keras.backend.epsilon(), 1 - tf.keras.backend.epsilon())
alpha_t = y_true * alpha + (tf.ones_like(y_true) - y_true) * (1 - alpha)
p_t = y_true * y_pred + (tf.ones_like(y_true) - y_true) * (tf.ones_like(y_true) - y_pred)
focal_loss_value = -alpha_t * tf.math.pow((tf.ones_like(y_true) - p_t), gamma) * tf.math.log(p_t)
return tf.reduce_mean(focal_loss_value)
return focal_loss_fixed
```
## 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).
### Performance
precision recall f1-score support
esca 0.79 0.97 0.87 480
healthy 0.99 0.90 0.94 1325
- Accuracy: 0.92
- Image AUC: 0.989
### 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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