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Pretrained_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6ddcc03cc1927fc884942c56dbda0902e43cdf36fa6434d6f1e31da155590c95
+size 658882872

README.md

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+# Plant Disease Classification Model
+
+This repository contains a pre-trained InceptionResNetV2 model for plant disease classification. The model can classify 38 different plant diseases and healthy conditions across various plant species.
+
+## Model Information
+
+- **Framework**: Keras/TensorFlow
+- **Architecture**: InceptionResNetV2
+- **Task**: Image Classification
+- **Number of Classes**: 38
+- **Input Shape**: (224, 224, 3) - RGB images
+- **Output**: Probability distribution over 38 classes
+
+## Supported Plant Species and Conditions
+
+The model can classify the following plant species and their conditions:
+
+### Apple
+- Apple scab
+- Black rot
+- Cedar apple rust
+- Healthy
+
+### Blueberry
+- Healthy
+
+### Cherry (including sour)
+- Powdery mildew
+- Healthy
+
+### Corn (maize)
+- Cercospora leaf spot Gray leaf spot
+- Common rust
+- Northern Leaf Blight
+- Healthy
+
+### Grape
+- Black rot
+- Esca (Black Measles)
+- Leaf blight (Isariopsis Leaf Spot)
+- Healthy
+
+### Orange
+- Haunglongbing (Citrus greening)
+
+### Peach
+- Bacterial spot
+- Healthy
+
+### Pepper, bell
+- Bacterial spot
+- Healthy
+
+### Potato
+- Early blight
+- Late blight
+- Healthy
+
+### Raspberry
+- Healthy
+
+### Soybean
+- Healthy
+
+### Squash
+- Powdery mildew
+
+### Strawberry
+- Leaf scorch
+- Healthy
+
+### Tomato
+- Bacterial spot
+- Early blight
+- Late blight
+- Leaf Mold
+- Septoria leaf spot
+- Spider mites Two-spotted spider mite
+- Target Spot
+- Tomato Yellow Leaf Curl Virus
+- Tomato mosaic virus
+- Healthy
+
+## Usage
+
+### Loading the Model
+
+```python
+import tensorflow as tf
+from huggingface_hub import hf_hub_download
+
+# Download the model from Hugging Face
+model_path = hf_hub_download(
+    repo_id="kero2111/Plant_Disease",
+    filename="Pretrained_model.h5"
+)
+
+# Load the model
+model = tf.keras.models.load_model(model_path)
+```
+
+### Making Predictions
+
+```python
+import numpy as np
+from PIL import Image
+
+# Load and preprocess image
+def preprocess_image(image_path):
+    img = Image.open(image_path)
+    img = img.resize((224, 224))
+    img = np.array(img) / 255.0
+    img = np.expand_dims(img, axis=0)
+    return img
+
+# Make prediction
+image = preprocess_image("path_to_your_image.jpg")
+prediction = model.predict(image)
+predicted_class = np.argmax(prediction[0])
+confidence = prediction[0][predicted_class]
+
+# Get class name
+classes = [
+    "Apple___Apple_scab", "Apple___Black_rot", "Apple___Cedar_apple_rust", "Apple___healthy",
+    "Blueberry___healthy", "Cherry_(including_sour)___Powdery_mildew", "Cherry_(including_sour)___healthy",
+    "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot", "Corn_(maize)___Common_rust_",
+    "Corn_(maize)___Northern_Leaf_Blight", "Corn_(maize)___healthy", "Grape___Black_rot",
+    "Grape___Esca_(Black_Measles)", "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)", "Grape___healthy",
+    "Orange___Haunglongbing_(Citrus_greening)", "Peach___Bacterial_spot", "Peach___healthy",
+    "Pepper,_bell___Bacterial_spot", "Pepper,_bell___healthy", "Potato___Early_blight",
+    "Potato___Late_blight", "Potato___healthy", "Raspberry___healthy", "Soybean___healthy",
+    "Squash___Powdery_mildew", "Strawberry___Leaf_scorch", "Strawberry___healthy",
+    "Tomato___Bacterial_spot", "Tomato___Early_blight", "Tomato___Late_blight", "Tomato___Leaf_Mold",
+    "Tomato___Septoria_leaf_spot", "Tomato___Spider_mites Two-spotted_spider_mite",
+    "Tomato___Target_Spot", "Tomato___Tomato_Yellow_Leaf_Curl_Virus", "Tomato___Tomato_mosaic_virus",
+    "Tomato___healthy"
+]
+
+print(f"Predicted: {classes[predicted_class]}")
+print(f"Confidence: {confidence:.2%}")
+```
+
+## Model Performance
+
+The model has been trained on a comprehensive dataset of plant disease images and can accurately classify various plant diseases and healthy conditions.
+
+## Requirements
+
+- TensorFlow 2.x
+- NumPy
+- PIL (Pillow)
+- huggingface_hub
+
+## License
+
+This model is provided for research and educational purposes.
+
+## Citation
+
+If you use this model in your research, please cite the original dataset and model architecture used. 

Readme.txt

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+Encoding classes name for deployment :
+'Apple___Apple_scab': 0, 'Apple___Black_rot': 1, 'Apple___Cedar_apple_rust': 2, 'Apple___healthy': 3, 'Blueberry___healthy': 4, 'Cherry_(including_sour)___Powdery_mildew': 5, 'Cherry_(including_sour)___healthy': 6, 'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot': 7, 'Corn_(maize)___Common_rust_': 8, 'Corn_(maize)___Northern_Leaf_Blight': 9, 'Corn_(maize)___healthy': 10, 'Grape___Black_rot': 11, 'Grape___Esca_(Black_Measles)': 12, 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)': 13, 'Grape___healthy': 14, 'Orange___Haunglongbing_(Citrus_greening)': 15, 'Peach___Bacterial_spot': 16, 'Peach___healthy': 17, 'Pepper,_bell___Bacterial_spot': 18, 'Pepper,_bell___healthy': 19, 'Potato___Early_blight': 20, 'Potato___Late_blight': 21, 'Potato___healthy': 22, 'Raspberry___healthy': 23, 'Soybean___healthy': 24, 'Squash___Powdery_mildew': 25, 'Strawberry___Leaf_scorch': 26, 'Strawberry___healthy': 27, 'Tomato___Bacterial_spot': 28, 'Tomato___Early_blight': 29, 'Tomato___Late_blight': 30, 'Tomato___Leaf_Mold': 31, 'Tomato___Septoria_leaf_spot': 32, 'Tomato___Spider_mites Two-spotted_spider_mite': 33, 'Tomato___Target_Spot': 34, 'Tomato___Tomato_Yellow_Leaf_Curl_Virus': 35, 'Tomato___Tomato_mosaic_virus': 36, 'Tomato___healthy': 37

config.json

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+{
+  "model_type": "keras",
+  "framework": "keras",
+  "task": "image-classification",
+  "model_name": "plant-disease-classifier",
+  "description": "A pre-trained InceptionResNetV2 model for plant disease classification",
+  "num_classes": 38,
+  "classes": [
+    "Apple___Apple_scab",
+    "Apple___Black_rot", 
+    "Apple___Cedar_apple_rust",
+    "Apple___healthy",
+    "Blueberry___healthy",
+    "Cherry_(including_sour)___Powdery_mildew",
+    "Cherry_(including_sour)___healthy",
+    "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot",
+    "Corn_(maize)___Common_rust_",
+    "Corn_(maize)___Northern_Leaf_Blight",
+    "Corn_(maize)___healthy",
+    "Grape___Black_rot",
+    "Grape___Esca_(Black_Measles)",
+    "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)",
+    "Grape___healthy",
+    "Orange___Haunglongbing_(Citrus_greening)",
+    "Peach___Bacterial_spot",
+    "Peach___healthy",
+    "Pepper,_bell___Bacterial_spot",
+    "Pepper,_bell___healthy",
+    "Potato___Early_blight",
+    "Potato___Late_blight",
+    "Potato___healthy",
+    "Raspberry___healthy",
+    "Soybean___healthy",
+    "Squash___Powdery_mildew",
+    "Strawberry___Leaf_scorch",
+    "Strawberry___healthy",
+    "Tomato___Bacterial_spot",
+    "Tomato___Early_blight",
+    "Tomato___Late_blight",
+    "Tomato___Leaf_Mold",
+    "Tomato___Septoria_leaf_spot",
+    "Tomato___Spider_mites Two-spotted_spider_mite",
+    "Tomato___Target_Spot",
+    "Tomato___Tomato_Yellow_Leaf_Curl_Virus",
+    "Tomato___Tomato_mosaic_virus",
+    "Tomato___healthy"
+  ]
+} 

inference.py

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+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import os
+
+def load_model(model_path):
+    """
+    Load the pre-trained plant disease classification model
+    """
+    try:
+        model = tf.keras.models.load_model(model_path)
+        print("Model loaded successfully!")
+        return model
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        return None
+
+def preprocess_image(image_path, target_size=(224, 224)):
+    """
+    Preprocess image for model inference
+    """
+    try:
+        # Load image
+        img = Image.open(image_path)
+        
+        # Convert to RGB if necessary
+        if img.mode != 'RGB':
+            img = img.convert('RGB')
+        
+        # Resize image
+        img = img.resize(target_size)
+        
+        # Convert to numpy array and normalize
+        img_array = np.array(img) / 255.0
+        
+        # Add batch dimension
+        img_array = np.expand_dims(img_array, axis=0)
+        
+        return img_array
+    except Exception as e:
+        print(f"Error preprocessing image: {e}")
+        return None
+
+def predict_disease(model, image_array):
+    """
+    Make prediction on preprocessed image
+    """
+    try:
+        # Make prediction
+        prediction = model.predict(image_array)
+        predicted_class = np.argmax(prediction[0])
+        confidence = prediction[0][predicted_class]
+        
+        return predicted_class, confidence, prediction[0]
+    except Exception as e:
+        print(f"Error making prediction: {e}")
+        return None, None, None
+
+def get_class_name(class_index):
+    """
+    Get class name from class index
+    """
+    classes = [
+        "Apple___Apple_scab", "Apple___Black_rot", "Apple___Cedar_apple_rust", "Apple___healthy",
+        "Blueberry___healthy", "Cherry_(including_sour)___Powdery_mildew", "Cherry_(including_sour)___healthy",
+        "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot", "Corn_(maize)___Common_rust_",
+        "Corn_(maize)___Northern_Leaf_Blight", "Corn_(maize)___healthy", "Grape___Black_rot",
+        "Grape___Esca_(Black_Measles)", "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)", "Grape___healthy",
+        "Orange___Haunglongbing_(Citrus_greening)", "Peach___Bacterial_spot", "Peach___healthy",
+        "Pepper,_bell___Bacterial_spot", "Pepper,_bell___healthy", "Potato___Early_blight",
+        "Potato___Late_blight", "Potato___healthy", "Raspberry___healthy", "Soybean___healthy",
+        "Squash___Powdery_mildew", "Strawberry___Leaf_scorch", "Strawberry___healthy",
+        "Tomato___Bacterial_spot", "Tomato___Early_blight", "Tomato___Late_blight", "Tomato___Leaf_Mold",
+        "Tomato___Septoria_leaf_spot", "Tomato___Spider_mites Two-spotted_spider_mite",
+        "Tomato___Target_Spot", "Tomato___Tomato_Yellow_Leaf_Curl_Virus", "Tomato___Tomato_mosaic_virus",
+        "Tomato___healthy"
+    ]
+    
+    if 0 <= class_index < len(classes):
+        return classes[class_index]
+    else:
+        return "Unknown"
+
+def main():
+    """
+    Main function to demonstrate model usage
+    """
+    # Model path (update this path to your model location)
+    model_path = "Pretrained_model.h5"
+    
+    # Check if model exists
+    if not os.path.exists(model_path):
+        print(f"Model file not found at: {model_path}")
+        print("Please ensure the model file is in the current directory or update the path.")
+        return
+    
+    # Load model
+    model = load_model(model_path)
+    if model is None:
+        return
+    
+    # Example usage with a sample image
+    # Replace 'sample_image.jpg' with your actual image path
+    sample_image_path = "sample_image.jpg"
+    
+    if os.path.exists(sample_image_path):
+        # Preprocess image
+        image_array = preprocess_image(sample_image_path)
+        if image_array is None:
+            return
+        
+        # Make prediction
+        predicted_class, confidence, all_predictions = predict_disease(model, image_array)
+        
+        if predicted_class is not None:
+            class_name = get_class_name(predicted_class)
+            print(f"\nPrediction Results:")
+            print(f"Predicted Class: {class_name}")
+            print(f"Confidence: {confidence:.2%}")
+            print(f"Class Index: {predicted_class}")
+            
+            # Show top 3 predictions
+            top_3_indices = np.argsort(all_predictions)[-3:][::-1]
+            print(f"\nTop 3 Predictions:")
+            for i, idx in enumerate(top_3_indices):
+                class_name = get_class_name(idx)
+                confidence = all_predictions[idx]
+                print(f"{i+1}. {class_name}: {confidence:.2%}")
+    else:
+        print(f"Sample image not found at: {sample_image_path}")
+        print("Please provide a valid image path to test the model.")
+
+if __name__ == "__main__":
+    main() 

requirements.txt

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+tensorflow>=2.8.0
+numpy>=1.21.0
+Pillow>=8.0.0
+huggingface_hub>=0.10.0