Sugarcane Leaf Disease Detection

[abhisheks008]

Deep Learning Simplified Repository (Proposing new issue)

:red_circle: Project Title : Sugarcane Leaf Disease Detection :red_circle: Aim : The aim of this project is to detect the disease from the sugarcane leaves using computer vision and deep learning methods. :red_circle: Dataset : https://www.kaggle.com/datasets/nirmalsankalana/sugarcane-leaf-disease-dataset :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. 😎

[tushtithakur]

Hi , I'm excited to contribute to this project. Could you please assign me? Looking forward to getting started! @abhisheks008

Full name : Tushti Thakur GitHub Profile Link : https://github.com/tushtithakur Email ID : [email protected] Approach for this Project : Implement different deep learning algorithms using the dataset, evaluate it and compare performance. What is your participant role? GSSoC 2024

[Basma2423]

Wanna give it a try, can you assign it to me? @abhisheks008

Full name: Basma Mahmoud GitHub Profile Link: Basma2423 Email ID: [email protected] Approach for this Project: Multiple Deep Learning pre-trained models + CNN What is your participant role? (Mention the Open Source program): GSSoC-2024 participant

Can you add the label for GSSoC, please? Thanks.

[atharv1707]

Hey! I am interested in this project. I have worked on similar project in the past, and I would like to apply some new techniques that I have learned in image classification problems.

Full name: Atharv Pal GitHub Profile Link: https://github.com/atharv1707 Email ID: [email protected] Approach for this Project:

1. Data Preprocessing

• Resize images to a consistent size suitable for deep learning models.
• Normalize pixel values to the range [0, 1].
• Augment the dataset to increase diversity and robustness, because the data set does seem small, and we can increase it.

2. Model Selection

• I am planning to consider architectures like Convolutional Neural Networks (CNNs) due to their effectiveness in image classification.

3. Model Training

• Compile the chosen model with appropriate loss function and optimizer.
• Train the model on the training set and validate its performance on the validation set.
• Fine-tune hyperparameters such as learning rate, batch size, and number of epochs to optimize model performance.
• Monitor training progress using metrics like accuracy, loss, and validation accuracy.

4. Model Evaluation

• We can Evaluate the trained model on the test set to assess its generalization performance.
• Also we can calculate evaluation metrics such as accuracy, precision, recall, and F1-score for each disease category.

5. Model Interpretation and Improvement Interpret the model's predictions to understand its behavior and identify areas of improvement such as Class Activation Maps (CAM)

What is your participant role? GSSoC-2024 Contributor

Thanks man!

[abhisheks008]

Hi @atharv1707 try to implement different algorithms (atleast 2-3) for this dataset. Compare them based on the accuracy scores and find out the best fitted model for this dataset/project.

Are you up?

[atharv1707]

Hi @atharv1707 try to implement different algorithms (atleast 2-3) for this dataset. Compare them based on the accuracy scores and find out the best fitted model for this dataset/project.

Are you up?

@abhisheks008 sure mate! I would love to experiment on this!

[abhisheks008]

Assigned to you @atharv1707. You can start working on it.

[atharv1707]

Hey @abhisheks008 ! I hope you are doing well. I wanted to update regarding the project i have chosen. I have trained one model using CNN architecture, which gave a accuracy of near 60-65%. I wanted to know what other techniques/algorithms should i implement as you mentioned above. Personally, i was thinking of experimenting with ensemble techniques to see how much of increament in accuracy we can achieve. If it's alright , I can proceed with the ensemble , or any suggestions are welcome

Peace!

[abhisheks008]

Hey @abhisheks008 ! I hope you are doing well. I wanted to update regarding the project i have chosen. I have trained one model using CNN architecture, which gave a accuracy of near 60-65%. I wanted to know what other techniques/algorithms should i implement as you mentioned above. Personally, i was thinking of experimenting with ensemble techniques to see how much of increament in accuracy we can achieve. If it's alright , I can proceed with the ensemble , or any suggestions are welcome

Peace!

You can go ahead with your approach. Let's see the accuracy score then I'll suggest something from my end.

[Pranali3103]

Hello @abhisheks008 Can you please assign me this issue , I can help with the trying other algorithms.

[abhisheks008]

Already assigned to other contributor.

[atharv1707]

Hey @abhisheks008 ! I know it took alot longer that it should have, but I finally did what I aimed to do. I achieved an accuracy of 82.59% using ensemble learning in this project. Kindly let me know what should I do further.

[abhisheks008]

Hey @abhisheks008 ! I know it took alot longer that it should have, but I finally did what I aimed to do. I achieved an accuracy of 82.59% using ensemble learning in this project. Kindly let me know what should I do further.

Need to achieve at least 90%

[atharv1707]

Hey @abhisheks008 ! I know it took alot longer that it should have, but I finally did what I aimed to do. I achieved an accuracy of 82.59% using ensemble learning in this project. Kindly let me know what should I do further.

Need to achieve at least 90%

Hey!! @abhisheks008 , I applied ensemble learning and achieved an ensemble accuracy of 90.55%. Let me know what's our next step

[abhisheks008]

Hey @abhisheks008 ! I know it took alot longer that it should have, but I finally did what I aimed to do. I achieved an accuracy of 82.59% using ensemble learning in this project. Kindly let me know what should I do further.

Need to achieve at least 90%

Hey!! @abhisheks008 , I applied ensemble learning and achieved an ensemble accuracy of 90.55%. Let me know what's our next step

Push that code for review.

[github-actions[bot]]

Hello @atharv1707! Your issue #456 has been closed. Thank you for your contribution!