This is a python tensorflow project on detecting disease in a potato leaf.
The dataset used for this project is obtained from Kaggle using the potato folders.
The dataset contains (256 x 256) RGB images of healthy, early blight, and late blight leaves
Examples
The overall samples are 2152, distributed as 1000 late blight, 1000 early blight, and 152 healthy. Those images were split into 70% for training, 20% for validation, and 10% for testing.
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten, Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
import pandas as pd
import numpy as np
IMAGE_SIZE = 256
CHANNELS = 3
BATCH_SIZE = 32
EPOCHS = 15
train = ImageDataGenerator(rescale = 1./255)
validation = ImageDataGenerator(rescale = 1./255)
train_data = train.flow_from_directory(TRAIN_PATH,
target_size=(IMAGE_SIZE,IMAGE_SIZE),
batch_size=BATCH_SIZE,
class_mode="categorical",
seed=2022)
val_data = validation.flow_from_directory(VAL_PATH,
target_size=(IMAGE_SIZE,IMAGE_SIZE),
batch_size=BATCH_SIZE,
class_mode="categorical",
seed=2022)
