PomegradeAI — AI-Powered Pomegranate Quality Grading System | Final Year Project with Source Code
PomegradeAI is an end-to-end AI final year project that uses a fine-tuned EfficientNetB0 deep learning model to classify pomegranates into three quality grades through a Flutter mobile app and Django REST API backend. It achieves 97.4% test accuracy.
Technology Used
Python | TensorFlow | Keras | EfficientNetB0 | OpenCV | Django | Django REST Framework | Flutter | Dart | SQLite | NumPy | Pillow | Jupyter Notebook
PomegradeAI — Pomegranate Quality Grading System
PomegradeAI is a production-ready AI final year project built for agricultural quality assessment. It combines deep learning, REST API development, and cross-platform mobile application development into a single full-stack solution. The system automatically classifies pomegranates into three standardized quality grades using a fine-tuned EfficientNetB0 convolutional neural network, achieving a test accuracy of 97.4 percent. Whether you are a BTech CSE student looking for an AI final year project with source code or an MCA student who wants a unique machine learning project, PomegradeAI covers every dimension of a modern, real-world AI system.
This project is listed on CodeAj Marketplace, where you get the complete source code, pre-built project report, and optional mentorship support until your final submission.
What PomegradeAI Does
A user opens the Flutter mobile application, captures or uploads an image of a pomegranate, and the app sends that image over HTTP to a Django REST API. The backend preprocesses the image, runs inference through the EfficientNetB0 model, and returns a quality grade, confidence score, and a detailed recommendation — all within seconds. Every result is stored in a local SQLite database and accessible through a grading history screen in the app.
Quality Grade Classification
The model classifies pomegranates into the following three grades, each with tailored agricultural recommendations:
- QA Class 1 — High Quality (Export Grade): Suitable for direct export, premium retail, and gift packaging. Target price range is 80 to 120 rupees per kilogram, with a shelf life of 15 to 20 days when stored at 5 to 8 degrees Celsius.
- QB Class 2 — Medium Quality (Processing Grade): Ideal for juice extraction, anardana (dried seeds), and local markets. Storage at 8 to 12 degrees Celsius gives a shelf life of 7 to 10 days.
- QC Class 3 — Low Quality (Utilization Grade): Directed toward peel extraction for tannins and dyes, animal feed, biogas, or composting. Should be processed within 2 to 3 days.
Deep Learning Model Architecture
The model is built on EfficientNetB0 pre-trained on ImageNet, with a custom classification head added on top. The architecture flows from a 224x224x3 input through the EfficientNetB0 backbone, followed by GlobalAveragePooling2D, BatchNormalization, a Dense layer of 256 units with ReLU activation and 0.5 dropout, and finally a Softmax output layer with three nodes corresponding to the three quality grades.
Training was done in two phases. Phase 1 froze the backbone and trained only the classification head for 14 epochs at a learning rate of 1e-4. Phase 2 unfroze the top backbone layers and fine-tuned for 19 additional epochs at 1e-5. Both phases used Adam optimizer, categorical cross-entropy loss, EarlyStopping with patience of 5, ReduceLROnPlateau, and ModelCheckpoint callbacks.
The dataset contains 769 images across three classes — 391 high-quality, 253 medium-quality, and 125 low-quality. Class imbalance was handled using balanced sample weights. Augmentation techniques including random rotations, zoom, brightness and contrast shifts, and horizontal and vertical flips were applied through a tf.data pipeline.
Project Features
- 97.4 percent test accuracy on a three-class pomegranate quality dataset
- Two-phase transfer learning strategy with EfficientNetB0
- Django REST API with endpoints for grading, history, stats, and health check
- Flutter mobile app compatible with Android and iOS
- Grade-specific agricultural recommendations with price ranges and storage guidance
- Grading history stored in SQLite database
- Aggregate statistics endpoint showing grade distribution and average confidence
- Test-time augmentation (TTA) support for improved robustness
- Grad-CAM visualization in the training notebook for model explainability
- Modular codebase with separate preprocessor, inference, and recommendation layers
Real-World Applications
PomegradeAI demonstrates how AI can transform traditional agricultural supply chains. The system has direct applications in:
- Export-quality fruit sorting and grading at packing houses
- Cold storage management with grade-based shelf life recommendations
- Reducing post-harvest losses by routing low-grade produce to value-added processing
- Integration into smart farming platforms and agri-tech mobile applications
- Academic and research purposes in computer vision applied to agriculture
System Architecture Overview
The project follows a client-server architecture. The Flutter mobile app acts as the client, communicating over HTTP with the Django REST backend. The backend handles image preprocessing using OpenCV and Pillow, runs the TensorFlow/Keras inference pipeline, queries and writes results to SQLite, and returns structured JSON responses to the app. The entire stack is containerizable and can be deployed on a Linux VPS with Gunicorn serving the Django application behind Nginx.
API Reference Summary
- POST /api/grade/ — Submit a pomegranate image for grading. Returns grade label, confidence, recommendation, and all class probabilities.
- GET /api/history/ — Retrieve the last 20 grading results.
- GET /api/stats/ — Get aggregate counts, average confidence, and grade distribution.
- GET /health/ — Backend health check endpoint.
Who Should Buy This Project
This project is best suited for BTech CSE, MCA, and BSc IT students who want a Python AI final year project with source code that stands out in viva and presentations. It covers computer vision, transfer learning, REST API development, and mobile app development — giving you a project that spans multiple domains and demonstrates real-world relevance.
You can explore more projects like this — including other machine learning final year projects and Flutter-based final year projects — on the CodeAj Marketplace. If you need a project on a different domain or a fully custom solution, check out our custom project development service.
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