- Video Demo
- Overview
- Motivation
- Data Collection
- Resnet(Transform Learning)
- Installation and Run
- Deployement on AWS
- Future scope of the Project
For any queries regarding about this project contact me
Link : https://www.linkedin.com/in/anil-l-b023631b6/
Screen.Recording.2021-10-07.at.11.21.41.PM.mov
I developed “🌿Cotton Plant Disease Prediction & Get Cure App” using Artificial Intelligence especially Deep learning.I know Farmer can’t solve Farm’s complex and even small problems due to lack of perfect education. So as AI enthusiastic I decided to solve this problem using the latest technology like AI.
Then I decide which algorithm is best to solve this problem and I select Transform Learning “Resnet” . I create my own Resnet architecture and it works well on the training and as well as testing dataset.
What to do when you are at home due to this pandemic situation? I started to learn Machine Learning and Deep Learning model to get most out of it. I came to know mathematics behind all supervised models,unspurervised models,CNN,ANN and RNN. Finally it is important to work on application (real world application) to actually make a difference. To get a experience you have to work thats the reason to perform my favourable work done.
Indian AI Prodcution just start to collect lots of images of cotton crop plants from farmer with there help it easy to collect accurate data we need expertise in that domain and for farmers it's helps a lot. Thank You Indian AI Prodcution(Credits)
Database Link : [https://drive.google.com/drive/folders/1vdr9CC9ChYVW2iXp6PlfyMOGD-4Um1ue]
In 2012 at the LSVRC2012 classification contest AlexNet won the the first price, After that ResNet was the most interesting thing that happened to the computer vision and the deep learning world.
Because of the framework that ResNets presented it was made possible to train ultra deep neural networks and by that i mean that i network can contain hundreds or thousands of layers and still achieve great performance.
The ResNets were initially applied to the image recognition task but as it is mentioned in the paper that the framework can also be used for non computer vision tasks also to achieve better accuracy.
Many of you may argue that simply stacking more layers also gives us better accuracy why was there a need of Residual learning for training ultra deep neural networks.
It gives me more than 94% accuracy on training and validation data set in just 20 epochs. I am trying to increase accuracy with more data and epochs.
For More Information About Resnet Check this Blog : [https://iq.opengenus.org/resnet50-architecture/]
Flask is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries. ... Extensions exist for object-relational mappers, form validation, upload handling, various open authentication technologies and several common framework related tools.
Flask Tutorial : [https://www.tutorialspoint.com/flask/index.htm]
The Code is written in Python 3.9 If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install the required packages and libraries, run this command in the project directory after cloning the repository:
Install Required Libraries
Step 1: pip install -r requirements.txt
Running Project
Step 2: Python app.py
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I used Jupyter NoteBook (Google Colab) for model training. used spyder for model deployment on the local system. To use Jupyter NoteBook and Spyder, just install anaconda.
Software Requirments
- Python == 3.7.7
- TensorFlow == 2.1.0
- Keras == 2.4.3
- NumPy == 1.18.5
- Flask == 1.1.2
Deployement Process going on...
- Optimize Flask app.py
- Add Extra Features
- Front-End