Yield Estimation of Longline Aquaculture by the Shadows of Buoys Based on UAV Orthophoto Image

Yield prediction in longline aquaculture is essential for evaluating environmental impacts, facilitating risk assessment, and promoting sustainable management in fisheries. However, since cultured organisms in longline aquaculture are submerged and cannot be directly observed, existing yield prediction approaches are mostly based on indirect environmental proxies, which often lead to unsatisfactory accuracy. The Shadow Geometry Inversion for Aquaculture (SGIA) method enables direct and accurate yield estimation in longline aquaculture by utilizing the submergence state of buoys to infer load, which is determined by the weight of the cultured organisms and estimated by shadow lengths combined with solar altitude angles and buoy physical parameters in high-resolution unmanned aerial vehicle (UAV) imagery. Experiments have been conducted in a water body located in Shanghai and Fuding to validate the effectiveness of the SGIA method. The best results were achieved under solar altitudes of 10–25° and calm water conditions. Under these conditions, the SGIA-predicted yields closely matched the measured loads in the Shanghai controlled experiment (R2 = 0.985, MAPE = 9.19%). In the Fuding field application, the model effectively captured spatial variations in buoy loads across the farming area, demonstrating its practicality and scalability for large-scale yield mapping in real aquaculture environments.