An integrated three-stream network model for discriminating fish feeding intensity using multi-feature analysis and deep learning.

Abstract

Feed costs constitute a significant part of the expenses in the aquaculture industry. However, feeding practices in fish farming often rely on the breeder's experience, leading to feed wastage and environmental pollution. To achieve precision in feeding, it is crucial to adjust the feed according to the fish's feeding state. Existing computer vision-based methods for assessing feeding intensity are limited by their dependence on a single spatial feature and manual threshold setting for determining feeding status constraints. These models lack practicality due to their specificity to certain scenarios and objectives. To address these limitations, we propose an integrated approach that combines computer vision technology with a Convolutional Neural Net-work (CNN) to assess the feeding intensity of farmed fish. Our method incorporates temporal, spatial, and data statistical features to provide a comprehensive evaluation of feeding intensity. Using computer vision techniques, we preprocessed feeding images of pearl gentian grouper, extracting temporal features through optical flow, spatial features via binarization, and statistical features using the gray-level co-occurrence matrix. These features are input into their respective specific feature discrimination networks, and the classification results of the three networks are fused to construct a three-stream network for feeding intensity discrimination. The results of our proposed three-stream network achieved an impressive accuracy of 99.3% in distinguishing feeding intensity. The model accurately categorizes feeding states into none, weak, and strong, providing a scientific basis for intelligent fish feeding in aquaculture. This advancement holds promise for promoting sustainable industry development by minimizing feed wastage and optimizing environmental impact.