# 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.

---
tags:
- image-classification
metrics:
- accuracy
license: mit
---