A Genetic Programming‐Based Method for Multi‐Object Detection in Mussel Farm Images

New Zealand's aquaculture industry is experiencing significant growth, driven by its carbon efficiency and the lack of spatial constraints on land. Green‐lipped mussels, a key farmed species, are grown on long lines supported by buoyant plastic floats. Managing tens of thousands of these floats is an operational challenge for mussel farmers. Accordingly, applying multi‐object detection to farm imagery is a promising way to improve crop monitoring and productivity. This paper presents a Genetic Programming (GP)‐based method for multi‐object detection in mussel farms, designed to address site‐specific challenges such as a high density of targets, high levels of partial occlusion, and substantial variation in apparent object size due to camera distance. The proposed method integrates image preprocessing techniques for object localisation and GP techniques for classification. In terms of detection performance, the proposed method surpasses YOLOv12 by achieving an F1 score of 95.2% compared with 88.6%. Although execution speed and bounding box precision could be further improved, the method demonstrates robustness, effectiveness, and practical applicability in mussel farms.