Poultry Diseases Detection

[abhisheks008]

Deep Learning Simplified Repository (Proposing new issue)

:red_circle: Project Title : Poultry Diseases Detection :red_circle: Aim : The aim of this project is to detect the problem statement using deep learning methods. :red_circle: Dataset : https://www.kaggle.com/datasets/kausthubkannan/poultry-diseases-detection :red_circle: Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

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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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:red_circle::yellow_circle: 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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:white_check_mark: To be Mentioned while taking the issue :

• Full name :
• GitHub Profile Link :
• Email ID :
• Participant ID (if applicable):
• Approach for this Project :
• What is your participant role? (Mention the Open Source program)

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Happy Contributing 🚀

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

[Disha-16]

@abhisheks008 abhisheks008Hi, I am Disha Mukhopadhyay, currently persuing BTech in Computer Science and Engineering (CSE). I have done an internship as an AI intern. and published an IEEE paper in Machine Learning domain, and also have some papers in proceeding in Machine Learning and data science domain. It will be very helpful if you could assign me this project, so that I can work on this project. Full name : Disha Mukhopadhyay GitHub Profile Link : https://github.com/Disha-16 Email ID : [email protected] Approach for this Project : 1.Data collection and preprocessing: In this section, we will preprocess the dataset, which includes resizing images, normalizing pixel values, encoding labels (diseases), handling missing or incomplete data, and augmenting the dataset if necessary to increase its size and variability. 2.Exploratory Data Analysis(EDA): Visual inspection, statistical summary, data distribution will be performed. 3.Model Selection and Deployment: Various deep learning model(CNNs, RNNs) will be chosen and implemented. 4.Model Training and evaluation: Each model will be trained on the dataset, and performance of each model will be displayed. 5.Model Comparison and Selection: Will analyze the performance of all models based on the metrics obtained and will Choose the model that shows the best balance between accuracy, generalizability, and computational efficiency. 6.Documentation and Reporting: A detailed report of the whole project will be submitted.

What is your participant role? (Mention the Open Source program): Contributor in GirlScript Summer Of Code'2024

[Santhosh-Siddhardha]

Hi @abhisheks008 ,

• Full Name: Lingamuneni Santhosh Siddhardha
• GitHub Profile Link: [Santhosh-Siddhardha]
• Email ID: [email protected]
• Participant ID: NA
• Approach for the Project: Data Preparation: Preprocess and augment image data for deep learning tasks. Model Implementation: Implement deep learning algorithms suitable for image analysis (e.g., Convolutional Neural Networks - CNNs). Model Training: Train the models using the prepared image dataset to detect poultry diseases. Model Evaluation: Evaluate model performance using appropriate metrics (e.g., accuracy, precision, recall). Documentation: Provide detailed documentation within the Model folder, including visualizations and insights from the deep learning experiments. Participant Role: GSSoC24 Contributor

[abhisheks008]

Hi @Santhosh-Siddhardha and @Disha-16 both of your approaches are brief. As per the code of conduct issues will be assigned on FCFS basis.

Issue assigned to @Disha-16. You can start working on it.

@Santhosh-Siddhardha you can look for other issues. Lots of open issues are there in the repo.

[Disha-16]

@abhisheks008 Thank you