Introduction:
Apples are one of the most important temperate fruit crops in the world. Foliar (leaf) diseases pose a major threat to the overall productivity and quality of apple orchards. The current process for disease diagnosis in apple orchards is based on manual scouting by humans, which is time-consuming and expensive.
Objective:
The main objective of the project is to develop machine learning-based models to accurately classify a given leaf image from the test dataset to a particular disease category, and to identify an individual disease from multiple disease symptoms on a single leaf image. The work focuses on, Neural Network such as CNN with image processing methods. CNN algorithm achieved 75 percent accuracy with 30 epochs.

Libraries Used:

import numpy as np
import pandas as pd
%matplotlib inline
import seaborn as sns
import matplotlib.pyplot as plt
import cv2
import os
import warnings
warnings.filterwarnings('ignore')
import tensorflow as tf
import random
import albumentations as A
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Dense,Activation,Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.models import Sequential
from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping

Data Preprocessing:
As we deal with this massive amount of data to use for deep learning, we find different ways in which we can enrich this data so we can eventually train, validate, and hyper tune our Convolution Neural Network.

Dataset: train.csv contains information about the image files available in train_images. Itcontains 18632 rows(images) with 2 columns i.e (image , labels ).
test.csv The test set images. This competition has a hidden test set: only three images are provided here as samples while the remaining 5,000 images will be available to your notebook once it is submitted.
https://www.kaggle.com/c/plant-pathology-2021-fgvc8/data

Feature Engineering:
We used Keras TensorFlow to optimizes image dataset. We used ImageDataGenerator and flow_from_dataframe functions to optimize image dataset.
HEIGHT = 128
WIDTH=128
SEED = 45
BATCH_SIZE= 64

Challenges we faced: Getting good accuracy was difficult therefore, below are the hyper tunning we performed:
We used Softmax activation function in Convolution Neural Network.
Increased epoch from 5 to 30 to get better accuracy.
learning_rate=0.001
Softmax Activation Function: The softmax function is a function that turns a vector of K real values into a vector of K real values that sum to 1. The input values can be positive, negative, zero, or greater than one, but the softmax transforms them into values between 0 and 1, so that they can be interpreted as probabilities. If one of the inputs is small or negative, the softmax turns it into a small probability, and if an input is large, then it turns it into a large probability, but it will always remain between 0 and 1. Modeling:
This project deals with Image data and is essentially a classification problem. The goal here is to train models to accurately classify a given leaf image from the test dataset to a particular disease category, and to identify an individual disease from multiple disease symptoms on a single leaf image.

Convolutional neural network (CNN):
A convolutional neural network, or CNN, is a deep learning neural network designed for processing structured arrays of data such as images. Convolutional neural networks are widely used in computer vision and have become the state of the art for many visual applications such as image classification and have also found success in natural language processing for text classification. A convolutional neural network is a feed-forward neural network, often with up to 20 or 30 layers. The power of a convolutional neural network comes from a special kind of layer called the convolutional layer. Types of convolutional neural networks
• AlexNet
• VGGNet
• GoogLeNet
• ResNet
Convolutional neural networks are distinguished from other neural networks by their superior performance with image, speech, or audio signal inputs. They have three main types of layers, which are:
• Convolutional layer
• Pooling layer
• Fully connected (FC) layer