kero2111/Plant_Disease

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

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

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.