Automatic Controlling Fish Feeding Machine using Feature Extraction of Nutriment and Ripple Behavior

• URL: http://arxiv.org/abs/2208.07011v1
• Date: Mon, 15 Aug 2022 05:52:37 GMT
• Title: Automatic Controlling Fish Feeding Machine using Feature Extraction of Nutriment and Ripple Behavior
• Authors: Hilmil Pradana and Keiichi Horio
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Controlling fish feeding machine is challenging problem because experienced fishermen can adequately control based on assumption. To build robust method for reasonable application, we propose automatic controlling fish feeding machine based on computer vision using combination of counting nutriments and estimating ripple behavior using regression and textural feature, respectively. To count number of nutriments, we apply object detection and tracking methods to acknowledge the nutriments moving to sea surface. Recently, object tracking is active research and challenging problem in computer vision. Unfortunately, the robust tracking method for multiple small objects with dense and complex relationships is unsolved problem in aquaculture field with more appearance creatures. Based on the number of nutriments and ripple behavior, we can control fish feeding machine which consistently performs well in real environment. Proposed method presents the agreement for automatic controlling fish feeding by the activation graphs and textural feature of ripple behavior. Our tracking method can precisely track the nutriments in next frame comparing with other methods. Based on computational time, proposed method reaches 3.86 fps while other methods spend lower than 1.93 fps. Quantitative evaluation can promise that proposed method is valuable for aquaculture fish farm with widely applied to real environment.

Related papers

• NutritionVerse-Direct: Exploring Deep Neural Networks for Multitask Nutrition Prediction from Food Images [63.314702537010355]
  Self-reporting methods are often inaccurate and suffer from substantial bias. Recent work has explored using computer vision prediction systems to predict nutritional information from food images. This paper aims to enhance the efficacy of dietary intake estimation by leveraging various neural network architectures.
  arXiv  Detail & Related papers  (2024-05-13T14:56:55Z)
• How Much You Ate? Food Portion Estimation on Spoons [63.611551981684244]
  Current image-based food portion estimation algorithms assume that users take images of their meals one or two times. We introduce an innovative solution that utilizes stationary user-facing cameras to track food items on utensils. The system is reliable for estimation of nutritional content of liquid-solid heterogeneous mixtures such as soups and stews.
  arXiv  Detail & Related papers  (2024-05-12T00:16:02Z)
• 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)
• FishMOT: A Simple and Effective Method for Fish Tracking Based on IoU Matching [11.39414015803651]
  FishMOT is a novel fish tracking approach combining object detection and objectoU matching. The method exhibits excellent robustness and generalizability for varying environments and fish numbers.
  arXiv  Detail & Related papers  (2023-09-06T13:16:41Z)
• 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)
• Computer Vision Methods for Automating Turbot Fish Cutting [117.44028458220427]
  This paper is about the design of an automated machine to cut turbot fish specimens. Machine vision is used to detect head boundary and a robot is used to cut the head.
  arXiv  Detail & Related papers  (2022-12-20T09:08:00Z)
• TempNet: Temporal Attention Towards the Detection of Animal Behaviour in Videos [63.85815474157357]
  We propose an efficient computer vision- and deep learning-based method for the detection of biological behaviours in videos. TempNet uses an encoder bridge and residual blocks to maintain model performance with a two-staged, spatial, then temporal, encoder. We demonstrate its application to the detection of sablefish (Anoplopoma fimbria) startle events.
  arXiv  Detail & Related papers  (2022-11-17T23:55:12Z)
• Enhancing Food Intake Tracking in Long-Term Care with Automated Food Imaging and Nutrient Intake Tracking (AFINI-T) Technology [71.37011431958805]
  Half of long-term care (LTC) residents are malnourished increasing hospitalization, mortality, morbidity, with lower quality of life. This paper presents the automated food imaging and nutrient intake tracking (AFINI-T) technology designed for LTC.
  arXiv  Detail & Related papers  (2021-12-08T22:25:52Z)
• A Survey of Fish Tracking Techniques Based on Computer Vision [11.994865945394139]
  This paper presents a review of the advancements of fish tracking technologies over the past seven years-2023. It explores diverse fish tracking techniques with an emphasis on fundamental localization and tracking methods. It also summarizes open-source datasets, evaluation metrics, challenges, and applications in fish tracking research.
  arXiv  Detail & Related papers  (2021-10-06T07:46:35Z)
• 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)

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.