Assessment of Feeding Intensity in Juvenile Asian Crucian Carp Using Binocular Vision and 2D–3D Spatial Feature Fusion

Accurate assessment of fish feeding intensity is essential for understanding feeding demand and optimising aquaculture efficiency. Fish feeding behaviour is inherently a 3D spatial activity, yet monocular vision can only provide 2D image information, capturing feature variations on a single projection plane. This makes it incapable of effectively characterising the fish’s positional and behavioural features in 3D space, thereby limiting the accuracy of feeding intensity assessment. Therefore, a binocular vision-based assessment method for fish feeding intensity via 2D and 3D spatial feature fusion is proposed. Binocular fish images are acquired from a lateral perspective. Fish targets are then segmented using image processing techniques, and target feature points are extracted. On this basis, the 2D spatial features of the fish are derived. To address missing feature points caused by fish overlap and occlusion in binocular images, a disparity map-based Missing Point Restoration (MPR) method is proposed for feature restoration and matching. Furthermore, a Weighted Delaunay Triangulation (WDT) algorithm is proposed to ensure accurate 3D feature computation. Notably, the extracted 2D and 3D spatial features have clear physical meanings and can directly characterise feeding-related spatial behaviour, thereby improving the interpretability of feeding intensity assessment. Finally, a classification model based on Tabular Prior-data Fitted Network (TabPFN) is constructed for assessing fish feeding intensity. By extracting 3D spatial features via binocular vision, the proposed method overcomes the inadequacy of 2D features in characterising fish vertical behaviour, effectively compensates for depth-related assessment errors, and improves feeding intensity assessment accuracy. The proposed method was tested on a real fish feeding dataset, achieving an accuracy of 95.92%, precision of 95.97%, recall of 95.95%, specificity of 98.63%, and F1-Score of 95.96%, indicating its potential for fish feeding intensity assessment.