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Plant Leaf Disease Detection Using Machine Learning and Image Processing Project for final year students
Description:
In this project, we first collect images of different types of infected, good, and seems to be infected plant leaves. Once the leaf dataset is prepared then, we apply an image processing algorithm to the images and predict the infected and not-infected plant leafs using Deep Learning and image processing.
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Video Demonstration Model Building
Deployment Streamlit Application
Steps Involved in Image Processing
Image Acquisition
Enhancement of image
Image Restoration
Color Image Processing
Compression
Segmentation
Algorithm used used
Using Deep Learning for Prediction along with Image processing such as Convolution Neural networks is mostly preferred neural network for image analysis.
Installation of the Project
Tensorflow:-Install TensorFlow in your local environment or anaconda environment using command-line by using the command:
$ pip install TensorFlow
Keras:- For detailed installation of Keras with TensorFlow read this:- https://keras.io/#installation
Scikit Learn:- If you are using Anaconda it is previously installed. https://scikit-learn.org/0.15/install.html
Pickle:- If you are using Anaconda it’s previously installed.
OpenCv:- Install OpenCV using the command –> pip install opencv-python
Note: As the dataset is large and huge, it is better to use Google Colaboratory for training the model and evaluating the model.
Source Code of Plant Leaf Disease Detection Project using Machine Learning and Image Processing
Click Here to Download the Source Code
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