AI-Based Fruit Freshness & Expiry Prediction Using Images

I am a final-year [login to view URL] CSE student. I need help in building my academic project: AI-Based Fruit Freshness and Expiry Prediction Using Images. This is for educational purposes only, not a commercial system. I only need a working prototype that I can demonstrate in my final-year viva/presentation. --- Project Objectives 1. Build a Deep Learning Model (CNN / YOLO + CNN) that can: Classify fruit images into Fresh, Ripe, Overripe, Spoiling, Spoiled. Predict the number of days remaining before expiry. 2. Create a simple GUI (Streamlit preferred) that allows: Upload of fruit images. Display of freshness class + expiry prediction. Highlight spoiled regions (Grad-CAM heatmap optional). 3. Use open datasets (e.g., Fruits-360 from Kaggle) or a small custom dataset. 4. Ensure the project runs on Google Colab (preferred) or my local PC. --- Project Timeline (Flexible: 1–2 Months) Week 1 – Dataset & Preprocessing Collect dataset (Kaggle + custom images). Preprocess: resize, normalize, augment. Deliverable: Preprocessed dataset + notebook. Week 2 – Base CNN Model Train CNN model for 5 freshness classes. Evaluate: accuracy, confusion matrix. Deliverable: Trained model + evaluation results. Week 3 – Expiry Prediction & Spoilage Visualization Add expiry prediction (days left). Implement Grad-CAM or saliency map for spoiled zones. Deliverable: Notebook with expiry prediction + heatmap. Week 4 – GUI Integration Build Streamlit app: upload → classify → show expiry days + spoilage heatmap. Deliverable: Running GUI demo. Week 5 – Final Submission Package Clean, well-commented Python code. Trained model weights. Mini project documentation (15–20 pages). Step-by-step setup instructions. Deliverable: Final ZIP with everything. Note: I am flexible if it takes 6–8 weeks, but weekly updates are required. --- Deliverables (Clear List) 1. Python source code (TensorFlow / PyTorch, OpenCV). 2. Preprocessed dataset (or dataset link). 3. Trained model weights. 4. Streamlit GUI app. 5. Mini documentation/report (Word/PDF). 6. Setup guide (to run on Colab / local PC). --- Skills Required Python Machine Learning / Deep Learning CNN (TensorFlow / PyTorch) Computer Vision (OpenCV) Streamlit / Flask --- Budget Platform Range: ₹1,500 – ₹12,500 INR Actual Student Budget: Around ₹4,000 – ₹6,000 INR Payment will be released milestone by milestone only, after demo proof. --- Important Notes This is a final-year academic project, not a professional/commercial system. I only require a working prototype with reasonable accuracy and GUI. Milestone payments only (no upfront full payment). Weekly updates (Colab link / screenshots) are mandatory.

Project ID: 39750521