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

Animals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2774
Author(s):  
Guangxu Wang ◽  
Akhter Muhammad ◽  
Chang Liu ◽  
Ling Du ◽  
Daoliang Li
Keyword(s):  
Deep Learning ◽  
Automatic Monitoring ◽  
Industrial Applications ◽  
Nondestructive Method ◽  
Fish Behavior ◽  
The Body ◽  
Fish Schools ◽  
Recirculating Aquaculture ◽  
Normal Behavior ◽  
Recirculating Aquaculture Systems

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.

scholarly journals An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 343
Author(s):  
Kim Bjerge ◽  
Jakob Bonde Nielsen ◽  
Martin Videbæk Sepstrup ◽  
Flemming Helsing-Nielsen ◽  
Toke Thomas Høye
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Low Cost ◽  
Light Trap ◽  
Automatic Monitoring ◽  
Light Sources ◽  
Monitoring Methods ◽  
Computer Vision System ◽  
Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

scholarly journals Anaerobic Mineralization of Recirculating Aquaculture Drum Screen Effluent for Use as a Naturally-Derived Nutrient Solution in Hydroponic Cropping Systems

Conservation ◽  
2021 ◽  
Vol 1 (3) ◽  
pp. 151-167
Author(s):  
Joseph Tetreault ◽  
Rachel Fogle ◽  
Todd Guerdat
Keyword(s):  
Nutrient Solution ◽  
Agricultural Waste ◽  
The United States ◽  
Anaerobic Treatment ◽  
Production Costs ◽  
Effluent Treatment ◽  
Limiting Factors ◽  
Fertilizer Treatment ◽  
Recirculating Aquaculture ◽  
Recirculating Aquaculture Systems

Operation and effluent treatment costs are limiting factors for the success of recirculating aquaculture systems (RAS) in meeting seafood demand in the United States. Adopting a capture-and-reuse waste management model similar to terrestrial agriculture farmers would allow RAS farmers to monetize effluent and offset production costs. The moisture content and nutrient profile of RAS effluent makes it a potential option for use as a hydroponic fertilizer. Treatment of RAS waste is needed to mineralize particulate-bound nutrients before becoming a viable hydroponic nutrient solution. Anaerobic treatment (AT), a method used by municipal and agricultural waste treatment facilities to reduce total solids, has been shown to successfully mineralize particulate-bound nutrients from RAS effluent. Continuously mixed anaerobic batch bioreactors were used to evaluate the degree to which AT may mineralize particulate-bound nutrients in solid RAS waste. Concentrations of twelve different macro- and micro-nutrients were analyzed in the waste before and after treatment. Effluent samples were analyzed to determine the fraction of each nutrient in the solid and aqueous forms. This study showed that AT is an effective method to mineralize particulate-bound nutrients in RAS effluent and the mineralization rate data may be used to design a pilot-scaled flow-through RAS effluent treatment system.

scholarly journals Zebrafish automatic monitoring system for conditioning and behavioral analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marta de Oliveira Barreiros ◽  
Felipe Gomes Barbosa ◽  
Diego de Oliveira Dantas ◽  
Daniel de Matos Luna dos Santos ◽  
Sidarta Ribeiro ◽  
...  
Keyword(s):  
Video Processing ◽  
Red Light ◽  
Automatic Monitoring ◽  
Automated System ◽  
Control Group ◽  
Automatic Monitoring System ◽  
Fish Schools ◽  
Detection And Tracking ◽  
Evaluation Strategies ◽  
Light Stimuli

AbstractStudies using zebrafish (Danio rerio) in neuro-behavioural research are growing. Measuring fish behavior by computational methods is one of the most efficient ways to avoid human bias in experimental analyses, extending them to various approaches. Sometimes, thorough analyses are difficult to do, as fish can behave unpredictably during an experimental strategy. However, the analyses can be implemented in an automated way, using an online strategy and video processing for a complete assessment of the zebrafish behavior, based on the detection and tracking of fish during an activity. Here, a fully automatic conditioning and detailed analysis of zebrafish behavior is presented. Microcontrolled components were used to control the delivery of visual and sound stimuli, in addition to the concise amounts of food after conditioned stimuli for adult zebrafish groups in a conventional tank. The images were captured and processed for automatic detection of the fish, and the training of the fish was done in two evaluation strategies: simple and complex. In simple conditioning, the zebrafish showed significant responses from the second attempt, learning that the conditioned stimulus was a predictor of food presentation in a specific space of the tank, where the food was dumped. When the fish were subjected to two stimuli for decision-making in the food reward, the zebrafish obtained better responses to red light stimuli in relation to vibration. The behavior change was clear in stimulated fish in relation to the control group, thus, the distances traveled and the speed were greater, while the polarization was lower in stimulated fish. This automated system allows for the conditioning and assessment of zebrafish behavior online, with greater stability in experiments, and in the analysis of the behavior of individual fish or fish schools, including learning and memory studies.

scholarly journals Classification of Shoulder X-ray Images with Deep Learning Ensemble Models

2021 ◽  
Vol 11 (6) ◽  
pp. 2723
Author(s):  
Fatih Uysal ◽  
Fırat Hardalaç ◽  
Ozan Peker ◽  
Tolga Tolunay ◽  
Nil Tokgöz
Keyword(s):  
Deep Learning ◽  
Performance Test ◽  
The Body ◽  
Test Accuracy ◽  
Cohen’S Kappa ◽  
X Ray ◽  
Cohen's Kappa ◽  
Auc Value ◽  
Magnetic Resonance Imaging Mri ◽  
Fully Connected

Fractures occur in the shoulder area, which has a wider range of motion than other joints in the body, for various reasons. To diagnose these fractures, data gathered from X-radiation (X-ray), magnetic resonance imaging (MRI), or computed tomography (CT) are used. This study aims to help physicians by classifying shoulder images taken from X-ray devices as fracture/non-fracture with artificial intelligence. For this purpose, the performances of 26 deep learning-based pre-trained models in the detection of shoulder fractures were evaluated on the musculoskeletal radiographs (MURA) dataset, and two ensemble learning models (EL1 and EL2) were developed. The pre-trained models used are ResNet, ResNeXt, DenseNet, VGG, Inception, MobileNet, and their spinal fully connected (Spinal FC) versions. In the EL1 and EL2 models developed using pre-trained models with the best performance, test accuracy was 0.8455, 0.8472, Cohen’s kappa was 0.6907, 0.6942 and the area that was related with fracture class under the receiver operating characteristic (ROC) curve (AUC) was 0.8862, 0.8695. As a result of 28 different classifications in total, the highest test accuracy and Cohen’s kappa values were obtained in the EL2 model, and the highest AUC value was obtained in the EL1 model.

Microscreen effects on water quality in replicated recirculating aquaculture systems

2015 ◽  
Vol 65 ◽  
pp. 17-26 ◽  
Author(s):  
Paulo Fernandes ◽  
Lars-Flemming Pedersen ◽  
Per Bovbjerg Pedersen
Keyword(s):  
Water Quality ◽  
Recirculating Aquaculture ◽  
Recirculating Aquaculture Systems

The Water Quality Management for Recirculating Aquaculture System by Applying Nano Adsorption Technology

2021 ◽  
Vol 21 (7) ◽  
pp. 3975-3979
Author(s):  
Min-Jin Hwang ◽  
Jeongmin Cha ◽  
Eun-Sik Kim
Keyword(s):  
Water Quality ◽  
Water Quality Management ◽  
Reduction Rate ◽  
Fish Farming ◽  
Analytical Relationship ◽  
Pumice Stone ◽  
Recirculating Aquaculture ◽  
Recirculating Aquaculture Systems ◽  
Aquaculture Water

In a fish farm, the water quality is important to ensure fish growth and farm productivity. However, the study of the quality of water using in aquaculture has been ignored until now. Although there are several methods to treat water, nanomaterials have not yet been applied for indoor fish farming because it may difficult to supply a sufficient amount of water, and the operating parameters have not been developed for recirculating aquaculture systems. Nanotechnology can be applied to treat water, specifically through adsorption and filtration, to produce drinking water from surface water and to treat wastewater by processing a high volume of effluent. The adsorption and filtration of seawater has also progressed to allow for desalination of seawater, and this is recognized as a necessary tool for extended treatment protocols of various types of seawater. This study investigated the treatment of aquaculture water using nano-porous adsorbents (e.g., pumice stone) to control the contaminants in seawater in order to maintain the water quality required for aquaculture. The results are used to derive an analytical relationship between the ionic species in aquaculture water, and this provides empirical parameters for a batch reactor for aquaculture. The quality of the influent and effluent for aquaculture is compared using time-series analyses to evaluate the reduction rate of ionic components and thus suggest the optimum condition for fish farming using bioreactor processes.

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