potato-disease-classifier

Potato Disease classifier Report

Data Source: Kaggle Plant Village | Kaggle

The dataset is having 2152 images in 3 folders early blight, late blight, and normal.

Target: Create an app where you put the image of the plant and it will identify whether plant has early blight, late blight or normal

Approach: will load the images into batches, preprocess it, create train test and split, do data augmentation and create a CNN model

Data Preprocessing

Resizing images:

Problem: All image are not same size

Approach: load the image using tf module image_from directory and define the image size and batch size.

Status Effective

Split the images into train test Val

Approach: Create a function which will shuffle the data and take 80% of it split rest 10% for validation and rest 10% for testing.

Status: Effective

Scale the data

Approach: Will divide each pixel with 255

Status: Effective

Data Augmentation

Approach: Will flip and rotate the images using tf RandomFlip and RandomRotation

Status: Effective

CNN Model:

• Layers: 14
• Convolution layer (3, 3)
• Max pooling (2, 2)
• Activation : Relu for input and hidden layer, Softmax for output layer
• Optimizer: Adam
• Loss function: Sparse categorical cross entropy
• Metrics: Accuracy
• Epochs: 41
• Callback: monitor val_loss and save the best

Model Accuracy:

• Training: 99.59%
• Validation: 98.96%
• Test: 99.22%

Deployment

Used Pycharm to create an app, streamlit module to create an API and deployed over Heroku platform.

Challenges: Take input of an image and convert into a trainable array.

Approach: Read the image using image.open module and convert it into array using tf img_to_array, resize and rescale the array and match the model input dimension (1, 256, 256, 3)

Status: Effective.