ml4sci/deepfalcon

We are pleased to announce the Machine Learning for Science (ML4SCI) 2021 Hackathon Competition that will take place from November 8-22, 2021. The ML4SCI competition is hosted by several campuses in the US including Puerto Rico and across the world, and is open to everyone. Participants will have the option of competing online or in-person. The hackathon will focus on applying machine learning techniques to scientific challenges, including those from the fields of physics, astronomy and planetary science.

Please find the competition poster at this link: https://bit.ly/3vOEtyN

The agenda for the hackathon can be found here: https://indico.cern.ch/event/1085878/

Anyone interested in learning more about machine learning techniques and trying their hand at the competition is welcome. Participants are encouraged to self-organize into small teams or work on their own to devise unique solutions to the challenge(s). The participants can work on the challenges on their own schedule. The competition will run for two weeks, however only a small fraction of that time is needed to obtain competitive results. Participants will have opportunities to interact with the organizers and with each other in person, via Zoom and on Slack. The kickoff meeting will be on Monday, November 8 (5 PM EDT) in person and on Zoom https://bit.ly/2YzCty3. There will also be an introductory machine learning lecture by Prof. Harrison Prosper on Tuesday, November 9 (2:30 PM EDT).Please see the Hackathon Slack page for in-person meeting details. Winners will receive certificates and prizes.

Interested participants can register via Slack at https://bit.ly/3arK2t2

There are six main challenges:

• Higgs Boson Challenge (Classification, General)

• Particle Images Challenge (Classification, Computer Vision)

• Strong Lensing Challenge (Multi-class Classification, Regression, Computer Vision)

• NMR Spin Challenge (Multi-Target Regression)

• Planetary Albedo (Regression, Image Analysis)

• Circumgalactic Medium (Dimensionality Reduction, Spectra)

Please find more details and example Jupyter notebooks inside each challenge folder.

Solutions are due on Monday, November 22 at 7pm EDT/6pm Central time. Winners will receive certificates and prizes. For any questions about the ML4SCI Hackathon please contact Prof. Sergei Gleyzer [email protected]

deepfalcon's Issues

Title: Implement R U-Net Model for Plant Disease Prediction

Overview:

We propose the development and integration of an R U-Net model for predicting plant diseases using a Kaggle dataset. The U-Net architecture, widely known for its effectiveness in biomedical image segmentation, can be adapted to agricultural applications to identify and predict various plant diseases from leaf images.

Motivation:

Plant diseases pose a significant threat to global food security, and early detection is crucial for mitigating crop losses. By leveraging the power of deep learning and the U-Net architecture, we can develop an efficient and accurate model to assist farmers and agronomists in diagnosing plant diseases early.

Dataset:

The Kaggle dataset used for this project includes a variety of leaf images categorized by different plant diseases. This dataset will be pre-processed and augmented to enhance model performance and generalization.

Proposed Implementation:

1. Data Preparation:

   • Load and explore the Kaggle dataset.
   • Pre-process the images (resizing, normalization).
   • Augment the dataset to increase diversity and robustness.

2. Model Architecture:

   • Implement the R U-Net model in PyTorch/TensorFlow.
   • Design the architecture to handle high-resolution images and capture fine-grained details of plant diseases.

3. Training:

   • Split the dataset into training, validation, and test sets.
   • Train the model using appropriate loss functions (e.g., Dice Loss, Cross-Entropy Loss).
   • Implement data augmentation techniques to improve model robustness.

4. Evaluation:

   • Evaluate the model using metrics such as IoU (Intersection over Union), accuracy, precision, and recall.
   • Generate confusion matrices and other visualizations to assess model performance.

5. Deployment:

   • Provide scripts for training and inference.
   • Develop a user-friendly interface (e.g., web app) for farmers and agronomists to upload leaf images and receive predictions.

Code Structure:

plant_disease_prediction
|__ data
|    |__ load_data.py # Code for loading and preprocessing the dataset
|    |__ augment_data.py # Code for augmenting the dataset
|    |__ __init__.py
|__ models
|    |__ runet.py # Implementation of the R U-Net model
|    |__ __init__.py
|__ training
|    |__ train.py # Script for training the model
|    |__ evaluate.py # Script for evaluating the model
|    |__ __init__.py
|__ utils
|    |__ metrics.py # Evaluation metrics
|    |__ visualizations.py # Visualization tools for data and results
|    |__ __init__.py
|__ inference
|    |__ predict.py # Script for making predictions on new images
|    |__ __init__.py
|__ app
|    |__ app.py # Code for the user interface
|__ README.md # Project overview and instructions

Request:

I am not a current Google Summer of Code (GSoC) contributor, but I am looking to apply as a GSoC collaborator next year. To better understand the project and contribute effectively, I would like to suggest the following enhancements:

1. Model Architecture: Suggestions on optimizing the R U-Net architecture for plant disease prediction.
2. Data Augmentation: Effective techniques for augmenting plant leaf images.
3. Evaluation Metrics: Additional metrics that can be used to evaluate model performance.
4. User Interface: Ideas for developing a user-friendly interface for deploying the model.

Your expertise and contributions would be highly valuable in making this project successful. Thank you for considering this proposal, and I look forward to collaborating with the community.

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