-
-
Notifications
You must be signed in to change notification settings - Fork 294
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Sugarcane Leaf Disease Detection #823
Conversation
…rning.ipynb to Sugarcane Leaf Disease DetectionModelensemble-learning.ipynb
Our team will soon review your PR. Thanks @atharv1707 :) |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
- Remove the Dataset/Model folder, not useful here.
- Rename the dataset.MD file to README.md
- Rename the readme.md file to README.md
Hey @abhisheks008! I've implemented the changes based on your suggestions. Could you please review them? Thanks! |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
hey @abhisheks008 I have deleted the file. Though the indentation seemed fine, I have edited the file suitably so that the issue doesn't come up again. Please review the changes |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Looks good to me. Approved @atharv1707
Pull Request for DL-Simplified 💡
Issue Title : Sugarcane Leaf Disease Detection
Closes: #456
Description:
In this pull request, I have thoroughly analyzed and processed the dataset through the following steps:
Exploratory Data Analysis (EDA):
Data Pipeline:
Model Training:
Trained multiple models to compare their performance based on accuracy metrics:
Ensemble Learning:
Evaluation:
This comprehensive approach has resulted in improved model performance and accuracy. I hope that these contributions are valuable to the project.
Type of change ☑️
What sort of change have you made:
How Has This Been Tested? ⚙️
The dataset was split into three parts with a 60:20:20 ratio for training, validation, and testing. Separate data generators were created for each subset. After training each model, I evaluated its performance against the testing dataset to ensure accurate training and robust performance. This approach confirmed that the models were correctly trained and capable of performing well on unseen data.
Checklist: ☑️