AI-Based Fruit Freshness & Expiry Prediction Using Images
Budget: ₹1,500 – ₹12,500 INR
I am a final-year B.Tech 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.
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.
Related categories:
Python
Project Management
OpenCV
Flask
Documentation
Data Visualization
Computer Vision
Streamlit
Model Deployment