📌Plant Disease Identification Model

The Plant Disease Identification Model utilizes Convolutional Neural Networks (CNN) to accurately identify plant diseases from leaf images. Trained on the Plant Disease Image Dataset, which includes 70,295 images in the training set and 17,572 images in the validation set, the model covers 38 different plant disease classes across 14 crops. It detects and classifies diseases such as Apple Scab, Tomato Blight, and Powdery Mildew, offering farmers a reliable tool for early disease detection.

Dataset

The Plant Disease Image Dataset, used for crop disease identification, consists of 70,295 plant images from the training set and 17,572 images from the validation set, covering a variety of 38 different plant disease classes. The images are standardized to a resolution of 128x128 pixels, and the dataset occupies approximately five gigabytes of storage space.

Model Architecture

For the Plant Disease Identification Model, a Convolutional Neural Network (CNN) architecture was employed. This CNN model was specifically trained for crop disease identification. Leveraging deep learning techniques, the CNN analyzes images of plant leaves to detect and classify diseases accurately. This model aids farmers in early disease detection and management, contributing to improved crop health and yield.

Key Features:

• Crop Specific: The model is designed to diagnose diseases for a specific set of crops.
• Disease Diagnosis: It can classify diseases based on images of leaves.
• Accuracy: The CNN model demonstrates high accuracy in identifying plant diseases, helping farmers and researchers detect issues early.

Supported Crops and Diseases:

• The model works with a predefined list of 15 crops.
• For each crop, the model is trained to detect and classify up to 38 specific diseases.

Since model is trained for specific crops only so it can diagnose those specific crops only. The List of Crops For which this model will be helpful is:

[ 'Apple',
'Blueberry',
'Cherry_(including sour)',
'Corn_(maize)',
'Grape',
'Orange',
'Peach', 'Pepper, _bell',
'Potato',
'Raspberry',
'Soybean',
'Squash',
'Strawberry',
'Tomato' ]

The crop which can be used for diagnosis can only diagnose specific disease for which the model is trained. The List of crop diseases on Which Model is trained on is:

Found 17572 files belonging to 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']

How it Works:

• The model uses images of plant leaves to detect symptoms of various diseases.
• It applies CNN-based image classification to identify the correct disease for a given crop.

👨‍💻 CONTRIBUTERS

• Mudit Gupta
• Anisha Asnani
• Kashish Khanna