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Leaf Disease Detection Using Deep Learning

Our "Leaf Disease Detection Using Deep Learning" project uses advanced CNN models to spot plant diseases directly from leaf images. This tool helps farmers catch issues early, reducing crop losses and boosting yields. With image enhancements and data augmentation, we’ve achieved high accuracy, making it a reliable solution for healthier crops and a great example of deep learning’s potential in agriculture.

Project Overview

Welcome to the "Leaf Disease Detection Using Deep Learning" project. This impressive project addresses the problem of diagnosing and categorizing leaf disease using deep learning methods.


From the perspective of recognition simplicity, this project really enhances the process of early detection for farmers who want to protect their crops or garden lovers who want to know how healthy their plants are. Thus, based on convolutional neural network (CNN) models such as VGG16, VGG19, and EfficientNet-B4, we simultaneously achieved high diagnostic accuracy of diseases from different plant species, ensuring correct identification of diseases and minimizing crop losses to improve yield.


Prerequisites

Before trying your hand at this particular project, you must have the following:

  • Python programming proficiency (from basic to intermediate level).

  • Understanding of the core principles of Machine Learning and Deep Learning.

  • Experience with TensorFlow and Keras for building and developing models.

  • Competence in Google Colab to execute the code and retrieve the datasets.

  • Basic knowledge of image processing and convolutional neural networks (CNNs).


Approach

This project approach is to use transfer learning techniques to detect leaf disease from images. We used three pre-trained models. Which are Vgg-16, Vgg-19 and Efficient-B4. Our models are trained with thousands of images of diseased leaves and healthy leaves. Which ultimately learns to distinguish between different categories with impressive accuracy.


The ultimate objective is to make disease diagnosis easy and alleviate the burden of plant diseases through the use of artificial intelligence.


Workflow and Methodology details

The broad overview of this project is as follows:

  • Data Collection: First, we gathered the datasets made of the images of healthy and infected leaf samples.

  • Data preprocessing: Once we gathered samples, the images were normalized and then divided into data for training and others for validation.

  • Model Building: Implementing transfer learning using the pre-trained models of VGG16, VGG19 and EfficientNet-b4

  • Training the Model: subsequently, models are then developed on training databases in a manner to efficiently diagnose leaf disease.

  • Evaluation: Carrying out model testing and its performance assessment.


The methodology includes:

  • CNNs for Feature Extraction: Implementing Convolution Layers for the Important Features on the Images.

  • Transfer Learning: Implementation of pre-trained models (VGG16, VGG19, Efficient-b4) that reduce the time for learning the target model.

  • Data Augmentation: Enhancement of the model performance via augmentation methods over the dataset.


Data Collection

For this research, the dataset was collected from the Kaggle platform, which included pictures of healthy as well as infected leaves. During the analysis of images, it was clear that there were classes with a lot fewer images than the rest, resulting in an unequal dataset.


We have a dataset that comprises 8320 training pictures and 2080 validation images. They are divided into 26 classes of plant leaves disease and healthy leaves. These pictures were divided according to the different crops, such as apples, corn, grapes, etc.


Data preparation

In our case, after labeling the dataset, images are resized to 128x128 pixels for easy fitting. OpenCV and TensorFlow allow images to be processed by:

  • Resizing and normalizing pixel values.

  • Label the images according to the respective class (disease or healthy).

  • Augmentation techniques like flipping, rotating, and zooming to balance the dataset.

  • Normalize the pixel values to speed up model training.


Data Preparation Workflow

  • Load images and corresponding labels.

  • Resize images to 128x128.

  • Normalize the pixels.

  • Divide the available data into training and validation data.


Code Explanation

Here's what is happening under the hood. Let's go through it step by step:


STEP 1:

Mounting Google Drive

Mount your Google Drive to access and save datasets, models, and other resources.

from google.colab import drive
drive.mount('/content/drive')

Package installation

Installs the TensorFlow packages for building and training machine learning and deep learning models.

!pip install tensorflow

Importing Libraries

Libraries like os, PIL, OpenCV, Matplotlib, and NumPy are imported for interacting with the file system, managing image input/output, processing, and visualization. Keras and TensorFlow are well-known libraries for constructing neural networks. Various layers, models, and utilities are employed to define, compile, and train deep learning models.

import os
import keras
import numpy as np
from tqdm import tqdm
from keras.models import Sequential
from keras.callbacks import ModelCheckpoint
from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras import optimizers
from keras.preprocessing import image
from PIL import Image,ImageOps
import cv2
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import confusion_matrix, classification_report
import matplotlib.pyplot as plt
import tensorflow
from tensorflow.keras import Model
from tensorflow.keras.layers import Input, BatchNormalization, ReLU, ELU, Dropout, Conv2D, Dense, MaxPool2D, AvgPool2D, GlobalAvgPool2D, Concatenate
import tensorflow as tf
import tensorflow.keras
from tensorflow.keras import models, layers
from tensorflow.keras.models import Model, model_from_json, Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from tensorflow.keras.callbacks import TensorBoard
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D, SeparableConv2D, UpSampling2D, BatchNormalization, Input, GlobalAveragePooling2D
from tensorflow.keras.regularizers import l2
from tensorflow.keras.optimizers import SGD, RMSprop
from tensorflow.keras.utils import to_categorical
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