Abnormal Water Quality Monitoring Based on Visual Sensing of Three-Dimensional Motion Behavior of Fish

Cheng, Shuhong; Zhao, Kao-peng; Zhang, Dianfan

doi:10.3390/sym11091179

Cited by 19 publications

(9 citation statements)

References 21 publications

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“…In particular, 3D images can provide more spatial information to overcome the problem of occlusion during movement, and the tracking accuracy rate reached 95%. In addition, the stress level of fish has been assessed by monitoring the abnormal trajectory of the fish [10,11]. This method is helpful for the early diagnosis of fish disease and optimizes management practices in aquaculture.…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition of Fish Behavior with a Fusion of RGB and Optical Flow Data Based on Deep Learning

Wang

Muhammad

Chang

et al. 2021

Animals

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The rapid and precise recognition of fish behavior is critical in perceiving health and welfare by allowing farmers to make informed management decisions on recirculating aquaculture systems while reducing labor. The conventional recognition methods are to obtain movement information by implanting sensors on the skin or in the body of the fish, which can affect the normal behavior and welfare of the fish. We present a novel nondestructive method with spatiotemporal and motion information based on deep learning for real-time recognition of fish schools’ behavior. In this work, a dual-stream 3D convolutional neural network (DSC3D) was proposed for the recognition of five behavior states of fish schools, including feeding, hypoxia, hypothermia, frightening and normal behavior. This DSC3D combines spatiotemporal features and motion features by using FlowNet2 and 3D convolutional neural networks and shows significant results suitable for industrial applications in automatic monitoring of fish behavior, with an average accuracy rate of 95.79%. The model evaluation results on the test dataset further demonstrated that our proposed method could be used as an effective tool for the intelligent perception of fish health status.

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Section: Introductionmentioning

confidence: 99%

Automatic Recognition of Fish Behavior with a Fusion of RGB and Optical Flow Data Based on Deep Learning

Wang

Muhammad

Chang

et al. 2021

Animals

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“…Fish movement in good water quality differs from those in abnormal water quality. Cheng et al (2019) monitored fish movements by two cameras combined a three-dimensional motion trajectory system with a Kuhn-Munkres algorithm. They showed that the learning model successfully reflected the water quality.…”

Section: Water Quality and Fish Behaviourmentioning

confidence: 99%

Water quality monitoring in recirculating aquaculture systems

Lindholm‐Lehto

2023

Aquaculture Fish & Fisheries

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Good water quality in recirculating aquaculture systems (RAS) is crucial for ensuring the successful growth and survival of reared species. So far, there are no regulations for which parameters should be measured in RAS, and each farmer decides which parameters to follow. Traditionally, water quality parameters have been measured at certain intervals with handheld sensors and laboratory analyses, which can be labour intensive. Currently, a variety of sensors and monitoring equipment is available, even for the real-time monitoring of water quality parameters. Internet of Things-based systems and artificial intelligence can be applied for the monitoring purposes which allows real-time measurements and warnings of critical situations. However, many of the modern systems need competent users and require regular maintenance and calibration. Changes in water quality also induces changes in fish behaviour, such as swimming activity, depth, acceleration and water quality can be assessed also based on these changes. In this review, water quality parameters, variety of sensors and monitoring technologies have been summarised to provide an overview of the current monitoring systems for water quality. Additionally, analytical methods for more advanced analyses have also been briefly summarised. Although there are several advanced options available for monitoring the basic water quality parameters, real-time measurements of more advanced parameters still required require further development. K E Y W O R D S aquaculture, fish behaviour, Internet of Things (IoT), monitoring equipment, recirculating aquaculture systems (RAS), water quality 1 INTRODUCTION Recirculating aquaculture systems (RAS) are land-based intensive aquaculture systems where water is re-used typically via mechanical and biological treatment to reduce the consumption of water and yet maintain adequate water quality (Martins et al., 2010). RAS utilises modern knowledge of biology, environmental sciences, mechanical engineering and information technology (Xiao et al., 2019). Circulating water is reused multiple times, and only a small proportion is replaced with clean water (Chun et al.

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“…The amount of overlap between the predicted and ground truth bounding boxes is indicated by the IoU value, which ranges from 0 to 1 as described in Fig. 6 [47]. There is no overlap between the boxes if the IoU is 0.…”

Section: B Evaluation Indicators Of Modelmentioning

confidence: 99%

Marigold Flower Blooming Stage Detection in Complex Scene Environment using Faster RCNN with Data Augmentation

Patel¹

2023

IJACSA

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In recent years, flower growing has developed into a lucrative agricultural sector that provides employment and business opportunities for small and marginal growers in both urban and rural locations in India. One of the most often cultivated flowers for landscaping design is the Marigold flower. It is also widely used to create garlands for ceremonial and social occasions using loose flowers. Understanding the appropriate stage of harvesting for each plant species is essential to ensuring the quality of the flowers after they have been picked. It has been demonstrated that human assessors consistently used a category scoring system to evaluate various flowering stages. Deep learning and convolutional neural networks have the potential to revolutionize agriculture by enabling efficient analysis of largescale data. In order to address the problem of Marigold flower stages detection and classification in complex real-time field scenarios, this study proposes a fine-tuned Faster RCNN with ResNet50 network coupled with data augmentation. Faster RCNN is a popular deep learning framework for object detection that uses a region proposal network to efficiently identify object locations and features in an image. The Marigold flower dataset was collected from three different Marigold fields in the Anand District of Gujarat State, India. The collection includes of photos that were taken outdoors in natural light at various heights, angles, and distances. We have developed and fine-tuned a Faster RCNN detection and classification model to be particularly sensitive to Marigold flowers, and we have compared the generated method's performance to that of other cutting-edge models to determine its accuracy and effectiveness.

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Abnormal Water Quality Monitoring Based on Visual Sensing of Three-Dimensional Motion Behavior of Fish

Cited by 19 publications

References 21 publications

Automatic Recognition of Fish Behavior with a Fusion of RGB and Optical Flow Data Based on Deep Learning

Automatic Recognition of Fish Behavior with a Fusion of RGB and Optical Flow Data Based on Deep Learning

Water quality monitoring in recirculating aquaculture systems

Marigold Flower Blooming Stage Detection in Complex Scene Environment using Faster RCNN with Data Augmentation

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