###### Abstract  
Smart fisheries, integrating advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and image processing, are pivotal in enhancing aquaculture efficiency, sustainability, and resource management by enabling real-time environmental monitoring, precision feeding, and disease prevention. However, underwater fish recognition faces challenges in complex aquatic environments, which hinder accurate detection and behavioral analysis. To address these issues, we propose a novel image instance segmentation framework based on a deep learning neural network, defined as the AASNet (Agricultural Aqua Segmentation Network). In order to improve the accuracy and real-time availability of fine-grained fish recognition, we introduce a Linear Correlation Attention (LCA) mechanism, which uses Pearson correlation coefficients to capture linear correlations between features. This helps resolve inconsistencies caused by lighting changes and color variations, significantly improving the extraction of semantic information for similar objects. Additionally, Dynamic Adaptive Focal Loss (DAFL) is designed to improve classification under extreme data imbalance conditions. Abundant experiments on two underwater datasets demonstrated that the proposed AASNet obtains an optimal balance between segmentation performance and efficiency. Concretely, AASNet achieves mAP scores of 31.7 and 47.4, respectively, on the UIIS and USIS dataset, significantly outperforming existing state-of-the-art methods. Moreover, AASNet achieves an inference image recognition speed of up to 28.9 ms/per, which is suitable for practical agricultural applications of smart fish farming. digital agriculture; intelligent fish farming system; deep learning neural network; underwater image segmentation; fine-grained visual recognition +
Footnote †: journal: Applied sciences  
0000-0001-5000-5000]J.J.Kong 00000-0001-5000-5000]Shunong Tang 0000-0001-5000-5000-5000-5000-5000-5000-5000-5000-50000-5000-5000-50000-50000-50000-50000-50000-50000-50000-50000-50000-50000-50000-50000-50000-50000-50000-50000-500000-50000-50000-5000 protect the ecological environment, and promote the sustainable use of fishery resources through precise feeding and water quality management [3]. Instance segmentation [4; 5; 6], as one of the key tasks in computer vision, has made significant progress. The goal of instance segmentation is to assign each pixel in an image to a specific object instance, enabling precise recognition and segmentation of different objects within complex scenes. With the growing demand for automation and precise monitoring in aquaculture, the deep learning community has increasingly focused on the development and application of underwater vision tasks. Underwater vision involves the analysis and interpretation of underwater images, which is critical for robotic vision systems and the navigation of autonomous underwater vehicles. In agricultural fish farming, underwater image instance segmentation plays a pivotal role in effectively identifying and detecting fish and their behaviors, thereby assisting in monitoring the farming environment and optimizing management decisions. This process is essential for applications such as health monitoring [7], population management, and stocking density control in aquaculture. Despite its significance, agricultural underwater image instance segmentation faces numerous challenges, making this task particularly complex. Firstly, variations in lighting and color in underwater scenes significantly affect the accuracy of image segmentation. Due to the scattering and absorption properties of underwater light, objects at different depths show varying colors and brightness levels [8]. These factors lead to inconsistencies in feature representation, making it difficult to maintain semantic consistency in the segmentation model under changing lighting conditions [9]. This inconsistency poses a major challenge to accurate object recognition and differentiation.