Research advances on fish feeding behavior recognition and intensity quantification methods in aquaculture

• URL: http://arxiv.org/abs/2502.15311v1
• Date: Fri, 21 Feb 2025 09:05:29 GMT
• Title: Research advances on fish feeding behavior recognition and intensity quantification methods in aquaculture
• Authors: Shulong Zhang, Daoliang Li, Jiayin Zhao, Mingyuan Yao, Yingyi Chen, Yukang Huo, Xiao Liu, Haihua Wang,
• Abstract summary: Fish feeding behavior recognition and intensity quantification plays a crucial role in monitoring fish health, guiding baiting work and improving aquaculture efficiency.<n>This paper reviews the research advances of fish feeding behavior recognition and intensity quantification methods based on computer vision, acoustics and sensors in a single modality.
• Score: 8.131167663249485
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As a key part of aquaculture management, fish feeding behavior recognition and intensity quantification has been a hot area of great concern to researchers, and it plays a crucial role in monitoring fish health, guiding baiting work and improving aquaculture efficiency. In order to better carry out the related work in the future, this paper firstly reviews the research advances of fish feeding behavior recognition and intensity quantification methods based on computer vision, acoustics and sensors in a single modality. Then the application of the current emerging multimodal fusion in fish feeding behavior recognition and intensity quantification methods is expounded. Finally, the advantages and disadvantages of various techniques are compared and analyzed, and the future research directions are envisioned.

Related papers

• Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction [64.4881275941927]
  We present the first comprehensive evaluation of trustworthiness in a continental-scale multi-task LSTM model. Our investigation uncovers systematic patterns of model performance disparities linked to basin characteristics. This work serves as a timely call to action for advancing trustworthy data-driven methods for water resources management.
  arXiv  Detail & Related papers  (2025-03-13T01:50:50Z)
• Livestock feeding behaviour: A review on automated systems for ruminant monitoring [33.7054351451505]
  This paper is the first tutorial-style review on the analysis of the feeding behaviour of ruminants. It assesses the main sensing methodologies and the main techniques to measure and analyse the signals associated with feeding behaviour. It also highlights the potentiality of automated monitoring systems to provide valuable information.
  arXiv  Detail & Related papers  (2023-12-03T13:42:55Z)
• WaterFlow: Heuristic Normalizing Flow for Underwater Image Enhancement and Beyond [52.27796682972484]
  Existing underwater image enhancement methods mainly focus on image quality improvement, ignoring the effect on practice. We propose a normalizing flow for detection-driven underwater image enhancement, dubbed WaterFlow. Considering the differentiability and interpretability, we incorporate the prior into the data-driven mapping procedure.
  arXiv  Detail & Related papers  (2023-08-02T04:17:35Z)
• Feeding control and water quality monitoring in aquaculture systems: Opportunities and challenges [0.5156484100374059]
  Monitoring the water quality and controlling feeding are fundamental elements of balancing fish productivity and shaping the fish growth process. Currently, most fish-feeding processes are conducted manually in different phases and rely on time-consuming and challenging artificial discrimination. This paper reviews the main control design techniques for fish growth in aquaculture systems, namely algorithms that optimize the feeding and water quality of a dynamic fish growth process.
  arXiv  Detail & Related papers  (2023-06-14T07:10:12Z)
• Predicting the long-term collective behaviour of fish pairs with deep learning [52.83927369492564]
  This study introduces a deep learning model to assess social interactions in the fish species Hemigrammus rhodostomus. We compare the results of our deep learning approach to experiments and to the results of a state-of-the-art analytical model. We demonstrate that machine learning models social interactions can directly compete with their analytical counterparts in subtle experimental observables.
  arXiv  Detail & Related papers  (2023-02-14T05:25:03Z)
• Towards The Creation Of The Future Fish Farm [3.8176219403982126]
  Fish farm environments support the care and management of seafood within a controlled environment. New technologies are constantly being implemented in this sector to enhance efficiency. This study demonstrates a proof-of-concept to signify the efficiency and usability of the future fish farm.
  arXiv  Detail & Related papers  (2023-01-02T21:41:06Z)
• Automatic Controlling Fish Feeding Machine using Feature Extraction of Nutriment and Ripple Behavior [0.0]
  We propose automatic controlling fish feeding machine based on computer vision using combination of counting nutriments and estimating ripple behavior. Based on the number of nutriments and ripple behavior, we can control fish feeding machine which consistently performs well in real environment.
  arXiv  Detail & Related papers  (2022-08-15T05:52:37Z)
• Vision-Based Food Analysis for Automatic Dietary Assessment [49.32348549508578]
  This review presents one unified Vision-Based Dietary Assessment (VBDA) framework, which generally consists of three stages: food image analysis, volume estimation and nutrient derivation. Deep learning makes VBDA gradually move to an end-to-end implementation, which applies food images to a single network to directly estimate the nutrition.
  arXiv  Detail & Related papers  (2021-08-06T05:46:01Z)
• Tuna Nutriment Tracking using Trajectory Mapping in Application to Aquaculture Fish Tank [0.0]
  Estimating a state of fishes in a tank and adjusting an amount of nutriments play an important role to manage cost of fish feeding system. Our approach is based on tracking nutriments on videos collected from an active aquaculture fish farm.
  arXiv  Detail & Related papers  (2021-03-10T06:02:19Z)
• Movement Tracks for the Automatic Detection of Fish Behavior in Videos [63.85815474157357]
  We offer a dataset of sablefish (Anoplopoma fimbria) startle behaviors in underwater videos, and investigate the use of deep learning (DL) methods for behavior detection on it. Our proposed detection system identifies fish instances using DL-based frameworks, determines trajectory tracks, derives novel behavior-specific features, and employs Long Short-Term Memory (LSTM) networks to identify startle behavior in sablefish.
  arXiv  Detail & Related papers  (2020-11-28T05:51:19Z)
• Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities [52.59080024266596]
  We present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition. We first introduce the multi-modality of the sensory data and provide information for public datasets. We then propose a new taxonomy to structure the deep methods by challenges.
  arXiv  Detail & Related papers  (2020-01-21T09:55:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.