Seed Quality Prediction using DL

[LitZeus]

Deep Learning Simplified Repository (Proposing new issue)

🔴 Project Title : Seed Quality Prediction (also developed an API )

🔴 Aim : To get to know the quality of the maize seeds to increase quality agro production for farmers eliminating the costs for testing

🔴 Dataset : Dataset is generated by us and will be uploaded here in the repo or external link will be given

🔴 Approach : The model used for prediction is a Convolutional Neural Network (CNN) trained on grayscale images of maize seeds, with the following architecture::

• Conv2D layers for feature extraction.
• MaxPooling2D layers for down-sampling.
• Dense layers for classification with a softmax activation function to output class probabilities.
  The model was trained using TensorFlow and Keras

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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🔴🟡 Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

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✅ To be Mentioned while taking the issue :

• Full name : Tejas Athalye
• GitHub Profile Link : https://github.com/LitZeus
• Email ID : [email protected]
• Participant ID (if applicable): n/a
• Approach for this Project : This project provides a Flask-based API to predict the quality of maize seeds using a Convolutional Neural Network (CNN) model with model and database. The API can classify seeds into three categories: broken, discolored, and pure, helping to automate the quality assessment process.
• What is your participant role? gssoc-ext, hacktoberfest-accepted

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[github-actions]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[abhisheks008]

Hi @LitZeus thanks for showing interest. You need to implement at least 3-4 deep learning models for this problem statement.

Assigning this issue to you.

[LitZeus]

@abhisheks008 Sure! thanks!