metadata
language:
- en
tags:
- image-classification
- plant-disease
- keras
- tensorflow
- computer-vision
- agriculture
license: mit
datasets:
- plant-disease-dataset
metrics:
- accuracy
library_name: keras
pipeline_tag: image-classification
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.