Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/durjaysamrat/app-based-solution-for-rice-plant-disease-detection-using-tensorflow

This project provides a valuable tool for farmers, enabling them to manage rice plant diseases effectively and improve crop yield. The combination of advanced deep learning techniques and mobile technology offers a practical solution for real-world agricultural challenges.
https://github.com/durjaysamrat/app-based-solution-for-rice-plant-disease-detection-using-tensorflow

android-app android-studio disease-detection farmers jupiter-notebook rice-diseases tensorflow-lite

Last synced: 16 days ago
JSON representation

This project provides a valuable tool for farmers, enabling them to manage rice plant diseases effectively and improve crop yield. The combination of advanced deep learning techniques and mobile technology offers a practical solution for real-world agricultural challenges.

Awesome Lists containing this project

README 

        
### App-Based Solution for Rice Plant Disease Detection using TensorFlow



![Untitled_Export_V1](https://github.com/user-attachments/assets/09131fb1-31d8-42fb-86ca-0f629213e45c)



Rice Plant Disease Detection Application-In this project we have mainly focused on 3 diseases namely Bacterial Blight, BrownSpot and SheathRot, i have uploaded the model and the code for it , use android studio and kotlin for the in built libraries for easy creation of the application , will upload the code for the application soon,Read this file for better understanding of the model



This project aims to develop a mobile application that assists farmers in identifying rice plant diseases in real-time using advanced deep learning techniques. The application focuses on three major rice diseases: Bacterial Blight, Brown Spot, and Sheath Rot. By leveraging the YOLOv5 deep learning framework, the app provides accurate disease detection and offers localized treatment recommendations.



### Key Features

- **Real-Time Disease Detection**: Quickly identify rice plant diseases using your mobile device.

- **High Accuracy**: Achieves a model accuracy of 98.8% for disease detection.

- **Optimized for Mobile**: The app is designed to work efficiently on mid-range smartphones.

- **Offline Capability**: Use the app without an internet connection.



### Project Highlights

- **Data Collection and Preparation**: High-resolution images of rice leaves were collected and categorized into four groups: Bacterial Blight, Blast, Brown Spot, and Healthy. Data augmentation techniques were used to enhance the model's generalization capability.

- **Model Architecture**: The model is based on DenseNet with Squeeze-and-Excitation (SE) blocks and depthwise separable convolutions to improve feature recalibration and reduce computational cost.

- **Model Training**: The model was trained using a categorical cross-entropy loss function, achieving an accuracy of 98.8%.

- **Mobile Deployment**: The trained model was converted to TensorFlow Lite for mobile deployment, with post-training quantization applied for size reduction and compatibility with mid-range devices.

- **Android Application**: Developed using Android Studio and Kotlin, the app integrates the TensorFlow Lite model for real-time inference and provides an interactive interface for users to upload or capture images for analysis.



### Technical Details

- **Frameworks/Tools**: TensorFlow Lite, Android Studio, Kotlin

- **Model Performance**: 

  - Bacterial Blight: 99.1%

  - Sheath Rot: 98.75%

  - Brown Spot: 98.50%

  - Healthy Leaves: 99.25%

  - Inference Time: Less than 1 second per image on a mid-range device

  - Battery Efficiency: Optimized for low energy consumption



### Conclusion

This project provides a valuable tool for farmers, enabling them to manage rice plant diseases effectively and improve crop yield. The combination of advanced deep learning techniques and mobile technology offers a practical solution for real-world agricultural challenges.