This project implements a deep learning-based pipeline to classify rice plant images into disease categories, paddy varieties, and plant age groups. It includes data preprocessing, exploratory analysis, model training using ANN, DNN, and CNN architectures, as well as deployment via a FastAPI backend and a Next.js frontend.

./
├── data/                           # Stores the training and test datasets
│   ├── train_images/               # Training dataset
│   │   ├── bacterial_leaf_blight/
│   │   ├── bacterial_leaf_streak/
│   │   └── ...
│   ├── test_images/                # Test dataset
│   └── meta_train.csv              # Metadata file
├── models/                         # Trained model files
│   ├── disease_classification_model.keras
│   ├── variety_identification_model.keras
│   └── age_prediction_model.keras
├── notebooks/                      # Jupyter notebooks for each task
│   ├── task0_exploratory_data_analysis.ipynb
│   ├── task1_disease_classification.ipynb
│   ├── task2_variety_identification.ipynb
│   └── task3_age_prediction.ipynb
├── prediction/                     # Final prediction
│   └── COSC2753_A2_S1_G7.csv
├── scripts/                        # Standalone scripts for model inference
│   ├── data/
│   ├── preprocessing.py
│   ├── task1_disease_classification.py
│   ├── task2_variety_identification.py
│   └── task3_age_prediction.py
├── client/                         # Frontend (Next.js)
└── server/                         # Backend (FastAPI)

• Python version: 3.10
• Install dependencies using:

pip install -r requirements.txt

• Ensure that the dataset is placed in the correct directory structure (as described above).
• If the dataset is missing, download it from one of the following sources:
  • RMIT Canvas Assignment
  • Kaggle Competition Dataset

cd notebooks/

• Run all cells in the following notebooks:
  • task0_exploratory_data_analysis.ipynb
  • task1_disease_classification.ipynb
  • task2_variety_identification.ipynb
  • task3_age_prediction.ipynb

cd scripts/

• Execute the model pipelines with:

python task1_disease_classification.py  

python task2_variety_identification.py  

python task3_age_prediction.py

• Frontend: https://rice-plant-disease-classification.vercel.app/
• API Docs: https://rice-plant-disease-classification.k-clowd.top/docs

Backend (FastAPI):

cd server  
pip install -r requirements.txt  
uvicorn main:app --host 0.0.0.0 --port 8000 --reload

Frontend (Next.js):

cd client  
npm install  
npm run dev