Adding a Paddy Disease Detection using Transfer Learning and CNN

[IkkiOcean]

🔴 Project Title:
Paddy Crop Disease Detection using Transfer Learning and CNN

🔴 Aim:
To build a model that detects diseases in paddy crops using Transfer Learning and CNN. The project will focus on identifying the most accurate algorithm by comparing several models.

🔴 Dataset:
Paddy Disease Dataset

🔴 Approach:
The project will involve trying 3-4 algorithms and implementing the models. Key steps:

1. Exploratory Data Analysis (EDA): Analyze the dataset by checking image quality, class distributions, and visualization of sample images.
2. Model Building:
   • Use Transfer Learning (pre-trained models such as EfficientNetV2 , MobileNetV2).
   • Implement CNN from scratch.
   • Compare the models based on performance metrics like accuracy, precision, recall, and F1 score.
   • Experiment with hyperparameter tuning and data augmentation for better results.
3. Comparison & Conclusion: Compare the results of the models to find the most effective one. Highlight the accuracy scores and model efficiency.

✅ To be Mentioned when taking the issue:

• Full name: Vivek Prakash
• GitHub Profile Link: https://github.com/IkkiOcean
• Email ID: vivekprakash.st@gmail.com
• Approach for this Project: CNN , EfficientNetV2 , MobileNetV2
• Participant Role: GSSOC-ext

[github-actions[bot]]

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

[abhisheks008]

Assigned @IkkiOcean

[github-actions[bot]]

Hello @IkkiOcean! Your issue #912 has been closed. Thank you for your contribution!