Development of a Novel AI-Based Fruit Ripeness Detection System using Synthetic Multispectral Data and Knowledge Distillation

Project Title

Development of a Novel AI-Based Fruit Ripeness Detection System using Synthetic Multispectral Data and Knowledge Distillation

Project Overview

We are developing an academic minor project focused on fruit ripeness detection using AI. The project uses synthetic multispectral data generation and a custom CNN-based knowledge extraction architecture, followed by knowledge distillation to an RGB-only model suitable for deployment.

The project already has:

Clear methodology
Defined novelties
Dataset strategy
Architecture design

We are looking for a machine learning freelancer to implement the project end-to-end as per the provided specifications.

Core Novelties (IMPORTANT – READ CAREFULLY)

This project includes two key novelties that must be followed exactly:

1️⃣ Synthetic Multispectral Dataset Generation (Data-Side Novelty)

Instead of using GANs or random noise, the dataset must be generated using a physically inspired, parametric spectral model, where:

Each sample is a 31-band multispectral tensor

Spectral reflectance curves are generated per ripeness class

Controlled parameter perturbations simulate biological variability

Spatial structure and sensor noise are added

Final data is stored as .npy spectral cubes of shape (31, 224, 224)

Expected dataset size:

2000–3000 synthetic multispectral samples

4 classes: Unripe, Mid-ripe, Ripe, Overripe

2️⃣ Non-Normal CNN Knowledge Extraction (Model-Side Novelty)

The teacher model must NOT be a plain CNN.

It must include:

Spectral–Spatial Dual-Path CNN

Spectral path using 1×1 convolutions for inter-band learning

Spatial path using 3×3 convolutions for texture learning

Band-wise attention mechanism (SE-style)

Learns wavelength importance dynamically

Final output: 4-class ripeness logits

This model acts as a spectral expert.

Knowledge Distillation

A lightweight RGB-based CNN student model

Trained using:

Cross-Entropy Loss (true labels)

KL-Divergence Loss (teacher soft logits)

The student model must work only on RGB images during inference

Scope of Work

The freelancer is expected to:

Implement synthetic multispectral data generation

Build and train the spectral teacher model

Implement the dual-path CNN with band-wise attention

Extract teacher logits for distillation

Build and train the RGB student model using knowledge distillation

Provide evaluation metrics (accuracy, confusion matrix)

Deliver clean, well-documented code
Tech Stack Requirements

Python

PyTorch

NumPy

OpenCV (basic)

Matplotlib / Seaborn (for plots)

GPU-friendly implementation preferred

Deliverables

Complete source code (well-structured)

Synthetic dataset generation script

Trained teacher and student models

Training logs and evaluation results

Brief README explaining how to run the project.
Please apply only if you are comfortable implementing custom CNN architectures and knowledge distillation from scratch (not pretrained-only solutions)