🌾 Overview

• GrainPalette is a web-based application that classifies rice grain types using deep learning.
• Built with Flask and MobileNetV2, it leverages transfer learning to fine-tune a pre-trained model for rice grain images.

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🧠 Technical Highlights

🔍 Transfer Learning

• Uses MobileNetV2, a lightweight CNN pre-trained on ImageNet.
• Fine-tuned on rice grain images to adapt to the specific classification task.
• Reduces training time and improves accuracy with limited data.

🛠️ Architecture & Tools

• Frameworks: Python, Flask, TensorFlow/Keras.
• Model File: rice.h5 – stores the trained model.
• Training Script: train.py – handles model training and evaluation.
• Visualization: Includes accuracy.png and loss.png to track model performance.

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📈 Features & Functionality

• Users upload rice grain images via the web interface.
• The model predicts the rice variety (e.g., Basmati, Jasmine, etc.).
• Results are displayed with confidence scores and visual feedback.

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🌱 Applications

• Agriculture: Helps farmers and agronomists identify rice types for crop planning.
• Education: Useful for teaching machine learning in agricultural contexts.
• Quality Control: Supports non-destructive testing of rice grains.

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📚 Supporting Resources

You can explore the full project, including code, documentation, and demo files on GitHub. It also includes:

• Project Report
• Project PPT
• HTML templates for results and history
• Training and evaluation plots

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Would you like me to help you turn this into a study guide or presentation? I can also explain how transfer learning works in more detail if you're curious!