129 lines
4 KiB
Markdown
129 lines
4 KiB
Markdown
# Tensorflow Grapevine Disease Detection
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## Description
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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:
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Black Rot, ESA (Net Blight), and Leaf Blight.
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## 📁 Dataset
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The dataset originates from [Kaggle](https://kaggle.com/datasets/rm1000/grape-disease-dataset-original), containing **9,027 images** of grapevine leaves. The diseases are categorized as:
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- **Black Rot**
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- **ESCA (Net Blight)**
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- **Leaf Blight**
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<<<<<<< HEAD
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The dataset is **well-balanced** with a slight overrepresentation of **ESCA** and **Black Rot**. All images are in **.jpeg format** with dimensions **256x256 pixels**.
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=======
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## Model Structure
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>>>>>>> fe70005a86f7095d5e60f104bd6a3e22f50c2dac
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 <br>
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 <br>
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## 📊 Model Architecture Selection
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We evaluated pre-trained models from [`keras applications`](https://keras.io/api/applications/#usage-examples-for-image-classification-models) to balance **accuracy**, **model size**, and **inference speed**. The selection criteria included:
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- **Maximize accuracy**
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- **Minimize size** (1/size)
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- **Maximize CPU speed** (1/CPU Time)
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The **score formula** used for selection was:
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$Score = \frac{Accuracy}{Size . CPU Time}$
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**Top Models (Score > 0.05):**
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1. **MobileNetV2** (Smallest: 14 MB, High Accuracy: 77%)
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2. **MobileNet** (Fastest: 22.6 ms)
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3. **NASNetMobile**
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4. **EfficientNetB0**
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**Conclusion:**
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`MobileNetV2` was chosen for its optimal balance between accuracy, size, and speed.
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<br>
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## 🍇 Grapevine Diseases
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### **Key Diseases:**
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1. **Black Rot**
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2. **ESCA (Net Blight)**
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3. **Leaf Blight**
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## 🤖 Model Structure
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### **Architecture:**
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```python
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# Auto Stop
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early_stopping = EarlyStopping(monitor="val_loss", min_delta=0.2, patience=10)
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# Model
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model = Sequential()
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model.add(tf.keras.applications.MobileNetV2(
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input_shape=(IMG_HEIGHT, IMG_WIDTH, CHANNELS),
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include_top=False,
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weights='imagenet'
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))
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model.add(tf.keras.layers.GlobalAveragePooling2D())
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model.add(tf.keras.layers.Dense(100, activation='relu'))
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model.add(tf.keras.layers.Dense(100, activation='relu'))
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model.add(tf.keras.layers.Dense(NUM_CLASSES, activation='softmax'))
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optimizer = tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE)
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model.compile(
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optimizer=optimizer,
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loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
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metrics=['accuracy']
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)
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```
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**Parameters:**
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- **Total params:** 7.12M (27.17 MB)
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- **Trainable params:** 2.36M (9.01 MB)
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- **Non-trainable params:** 34.11K (133.25 KB)
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## 🛠️ Training Details
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- **Batch Size:** 32
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- **Epochs:** 100 (reduced to 25 via early stopping)
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- **Data Augmentation:** Not used (insufficient improvement in accuracy)
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- **Normalization:** Pixel values normalized to [0, 1]
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## 📊 Results
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### **Performance:**
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- **Validation Accuracy:** ~99.9%
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- **Confusion Matrix Analysis:**
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- Model biased toward **ESCA** and **Healthy** classes.
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- Suspected causes:
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1. Original dataset imbalance
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2. Similar visual features across diseases
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### **Prediction Example:**
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### **Attribution Mask:**
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- **Key Insight:** Model focuses on leaf shape rather than disease-specific features (e.g., black spots).
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### 📚 ressources:
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https://www.kaggle.com/code/ahmedmsaber/grape-leafs-diseases-mobilenetv2-val-acc-99 <br>
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https://www.tensorflow.org/tutorials/images/classification?hl=en <br>
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https://www.tensorflow.org/lite/convert?hl=en <br>
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https://www.tensorflow.org/tutorials/interpretability/integrated_gradients?hl=en <br>
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🤖AI(s) : deepseek-coder:6.7b | deepseek-r1:8b
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