Genetic Programming-Based Multi-Object Matching for Mussel Floats in Mussel Farm Images

The demand for Artificial Intelligence (AI)-powered solutions in New Zealand's aquaculture sector, particularly in mussel farms, is fast growing due to the need for enhanced productivity, reduced carbon footprint, and a safer worksite. An effective crop monitoring system that relies on high-resolution images from boat-mounted cameras requires an effective multi-object matching module. This paper presents a new Genetic Programming (GP)-based multi-object matching approach for mussel floats in farm images, addressing site-specific challenges such as a large number of tracklets, near-identical appearances, and low-overlapping scenes. Current object matching methods, including appearance models, motion models, and transformer models, struggle under these conditions. Evaluated on mussel farm images from the Marlborough Sounds of New Zealand, the proposed GP method demonstrates significantly higher matching accuracy, achieving 96.4% compared to 39.6% by a state-of-the-art transformer model. Our GP method achieves accurate multi-object matching in both slow and fast-moving scenarios, outperforming the state-of-the-art particularly in the latter.