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Crop Analyzer Data Modeling
Is your feature request related to a problem? Please describe.
No CODE_OF_CONDUCT.md file is present.
Describe the solution you'd like
@gshanbhag525 As a GSSOC participant I would like to add a CODE_OF_CONDUCT.md file in this project. Using this as a reference.
Additional context
Tensorflow setup for windows is added.
Linux users need TensorFlow setup and an installation guide to work with TensorFlow.
Create a requirements text file for this project which will contain all the required libraries and frameworks for doing the data modelling.
Update Frontend/README.md
Refer this issue: CropAi/Frontend#13
Under heading IMPORTANT LINKS, add details about data-modeling repo in brief (tech-stack, techniques, dataset etc.), give it a heading (eg. Data-Modeling)
the link to repo should be there
detect spots on plant leaves using Opencv and image segmentation techniques
Create a keras Convolutional Neural Network on Alexnet arch. Create model checkpoints in the code.
Create documentation for doing EDA Analysis on the dataset.
Create 2 folders
modules
Put these python module files in the modules folder. eg. "Image_To_array.py" , "label_binarizer_instance.py"
docs
Put these markdown files in the docs folder. eg. "transfer learning.md"
Extract "Installtion steps for tensorflow" from README.md and paste them in the docs folder as two separate md files for each OS
Give links to these files in the Readme file.
Remove duplicate parts. Follow the same visual appearance of Linux Installationfor Windows Installation
Use the keras model created to predict the new test images and the disease class it follows.
Display the image too using matplotlib library.
I am a GSSOC participant and I would like to add CONTRIBUTING.md file to include the following mentioned features
Create a standardized python module for loading and splitting the dataset loaded from h5 file into train and validation splits (do not forget to set seed).
Create a py file and Load .h5 file from google drive.
Visualize the training process for the model created using keras .
Use matplotlib and seaborn libraries to display the training graph .
You can take inspiration from Tensorboard.
Use Image segmentation techniques to detect spots on leaves. Use deep learning.
Create a python module for reading images from the directory, their labels and saving the data in an h5 file.
Within the module, create a function for loading the whole dataset
Create a method to Convert each imported image into an Array so that it can be used in further modelling.
use image segmentation techniques to create a google collab notebook which consists of UNet model .
Use pickle library to label binarizer instance and print the issues
Create a python module to detect the spots on plant leaves. Use image segmentation techniques.
Use Kmeans algorithm
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π #87 segmentation of image, by abhishekgupta368
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π #87 segmentation of image, by abhishekgupta368
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Add documentation as to how you can use deep learning/ transfer learning for this project.
Try to implement a model using siamese network which will give better accuracy at lesser epoch
This github action marks stale issues and pull requests i.e. the issues or PR which are unattended for a long period of time.
We require this file in our backend repository, so you should make a pull request for this issue :
CropAi/Backend#11
Use Scikit-learnβs Label Binarizer to convert each image label to binary label.
Remove the separate folder created for tf installtion guide on windows. Create a heading in readme.md file called "Installtion Guide" and Add Subheadings as Tensorflow Installtion on Windows.
Get rid of .ipynb_checkpoints by adding the hidden directory to gitignore file.
I'd like to add some intermediate level github actions to this project.
Refine label images module and add metjod called predict and return the predictions
Write a python script to load 200 images from each folder of plants dataset.
Create a documentation for how to setup the dataset to work with google collab.
Give detailed steps.
The model built in tf.keras is present in jupyter file in this repo. It is present google collab.
You can refer it too.
Fine tune Keras model to get correct predictions for all the 15 categories. The model is present in CropAI_keras jupyter notebook.
Build a model using tf.keras and also display the confusion matrix for the given model.
Tweak the current architecture, and use some efficient way of saving space when saving this model file. [preferred size : ~70-75 MB]
Write and save label encoder file in training and predict script
You have to write detailed steps as how to setup tesnorflow on windows machine for data modelling.
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