Yanis
e34e0db34c
Symptom: a guest scanning a plant fired POST /api/mobile/scans without a Bearer token, the backend rightfully replied 401, and the apiPost emitter dispatched 'unauthorized' which AuthContext interpreted as "session lost" and wiped the local guest, kicking the user back to Onboarding. Two fixes: 1. apiGet/apiPost now track whether a Bearer was actually attached to the request and only emit the 'unauthorized' event when one was sent. An anonymous 401 stays a plain SERVER error. 2. pushScan() short-circuits if getToken() returns null, so guests never even hit the network for scan persistence. Combined effect: guests stay guests, registered users still get session-revocation feedback when their token is rejected. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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| VinEye | ||
| vineye-admin | ||
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| AGENTS.md | ||
| CLAUDE.md | ||
| package.json | ||
| pnpm-lock.yaml | ||
| PROJECT_SUMMARY.md | ||
| README.md | ||
Tensorflow Grapevine Disease Detection
Description
This project develops a mobile application for detecting diseases on grapevines using a Deep Learning model. The implementation leverages TensorFlow and Keras to build a CNN-based classifier for identifying three common diseases: Black Rot, ESA (Net Blight), and Leaf Blight.
📁 Dataset
The dataset originates from Kaggle, containing 9,027 images of grapevine leaves. The diseases are categorized as:
- Black Rot
- ESCA (Net Blight)
- Leaf Blight
The dataset is well-balanced with a slight overrepresentation of ESCA and Black Rot. All images are in .jpeg format with dimensions 256x256 pixels.
📊 Model Architecture Selection
We evaluated pre-trained models from keras applications to balance accuracy, model size, and inference speed. The selection criteria included:
- Maximize accuracy
- Minimize size (1/size)
- Maximize CPU speed (1/CPU Time)
The score formula used for selection was:
Score = \frac{Accuracy}{Size . CPU Time}
Top Models (Score > 0.05):
- MobileNetV2 (Smallest: 14 MB, High Accuracy: 77%)
- MobileNet (Fastest: 22.6 ms)
- NASNetMobile
- EfficientNetB0
Conclusion:
MobileNetV2 was chosen for its optimal balance between accuracy, size, and speed.
🍇 Grapevine Diseases
Key Diseases:
- Black Rot
- ESCA (Net Blight)
- Leaf Blight
🤖 Model Structure
Architecture:
# Auto Stop
early_stopping = EarlyStopping(monitor="val_loss", min_delta=0.2, patience=10)
# Model
model = Sequential()
model.add(tf.keras.applications.MobileNetV2(
input_shape=(IMG_HEIGHT, IMG_WIDTH, CHANNELS),
include_top=False,
weights='imagenet'
))
model.add(tf.keras.layers.GlobalAveragePooling2D())
model.add(tf.keras.layers.Dense(100, activation='relu'))
model.add(tf.keras.layers.Dense(100, activation='relu'))
model.add(tf.keras.layers.Dense(NUM_CLASSES, activation='softmax'))
optimizer = tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE)
model.compile(
optimizer=optimizer,
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy']
)
Parameters:
- Total params: 7.12M (27.17 MB)
- Trainable params: 2.36M (9.01 MB)
- Non-trainable params: 34.11K (133.25 KB)
🛠️ Training Details
- Batch Size: 32
- Epochs: 100 (reduced to 25 via early stopping)
- Data Augmentation: Not used (insufficient improvement in accuracy)
- Normalization: Pixel values normalized to [0, 1]
📊 Results
Performance:
- Validation Accuracy: ~99.9%
- Confusion Matrix Analysis:
- Model biased toward ESCA and Healthy classes.
- Suspected causes:
- Original dataset imbalance
- Similar visual features across diseases
Prediction Example:
Attribution Mask:
- Key Insight: Model focuses on leaf shape rather than disease-specific features (e.g., black spots).
📚 ressources:
https://www.kaggle.com/code/ahmedmsaber/grape-leafs-diseases-mobilenetv2-val-acc-99
https://www.tensorflow.org/tutorials/images/classification?hl=en
https://www.tensorflow.org/lite/convert?hl=en
https://www.tensorflow.org/tutorials/interpretability/integrated_gradients?hl=en
🤖AI(s) : deepseek-coder:6.7b | deepseek-r1:8b