naman-0804/Okra_Maturity_Analysis

# Okra Maturity Analysis ## Project Overview This project aims to analyze the maturity of okra plants using thermal imaging and machine learning. It leverages a pre-trained TensorFlow Lite model to classify the maturity stage of okra plants based on their thermal images. ## Features - **Thermal Image Classification:** Utilizes a TensorFlow Lite model to categorize okra plant maturity into stages such as "young", "developed", and "average". - **Model Training:** The project includes a Python script (`okra_model_trainer.py`) for training the machine learning model. - **Model Deployment:** A TensorFlow Lite model (`image_maturity_model.tflite`) is provided for deployment and inference. - **Streamlit Web App:** A Streamlit web application (`streamlit_site.py`) allows for interactive analysis and visualization of thermal images. ## Installation 1. **Install dependencies:** ```bash pip install -r requirements.txt ``` ## Usage 1. **Train the model :** ```bash python okra_model_trainer.py ``` 2. **Run the Streamlit web app:** ```bash streamlit run streamlit_site.py ``` 3. **Run the inference script with the pre-trained model:** ```bash python Run_with_model.py ``` This will load the pre-trained model (`image_maturity_model.tflite`) and apply it to the thermal image data located in the `Thermal_image` directory. **Note:** The `Thermal_image` directory contains image data labeled with maturity stages ("young", "developed", and "average"). These images serve as the input for the model.