Overview
This project aims to transform the prawn sorting process by introducing hyperspectral imaging (HSI) that distinguishes between soft-shell and hard-shell prawns using their spectral images processed by ML. Current manual methods, which rely on tactile assessment, are subjective, labour-intensive, and inconsistent. By utilising HSI and ML, this research seeks to improve the accuracy, reliability, and efficiency of shell hardness detection.

Objective
The Calibration Phase of this project focuses on developing and validating ML models capable of analysing HSI data to classify prawn shell hardness levels. These models will lay the groundwork for implementing systems in the next phase, ensuring consistent, high-quality products for the market.

Methodology
The project will:

- Rent and deploy a Resonon PIKA L HSI camera, selected based on prior proof-of-concept findings.
- Collect and classify 2,400 prawn samples from the host farm, APF, based on shell hardness and size.
- Generate spectral and morphological data, creating a robust dataset for ML training and validation.
- Develop ML models to correlate spectral signatures with shell hardness levels.
- Validate ML models against independent samples to ensure reliability.
Budget Highlights
The project requests $112,583 from FRDC for salaries, operational expenses, and HSI camera rental. DPI will contribute an additional $35,437 and the host farm will also contribute $6,650 for labour, prawn, packaging and shipping, bringing the total project cost to $154,670.

Team
The project team is led by Dr. Iman Tahmasbian, a Senior Scientist at Queensland DPI with extensive expertise in hyperspectral imaging (HSI) and a strong track record in R&D and high-impact publications. Supported by DPI’s state-of-the-art HSI and food laboratories, the team also includes Dr. Jing Wang and an AgTech data scientist, who are experts in image processing, data analysis, and ML.

Next Steps
Upon successful calibration of the ML models, the project will move to the Implementation Phase, focusing on integrating the developed technology into sorting systems for prawn farms. The implementation phase is not part of this project and will be a future funded project.

This project represents a vital step towards modernising the Australian prawn industry by combining advanced imaging and ML technologies, ensuring a more sustainable, profitable, and high-quality production system.