Using Drones to Assess Volitional Swimming Kinematics of Manta Ray Behaviors in the Wild

Fong, Vicky; Hoffmann, Sarah L.; Pate, Jessica H.

doi:10.3390/drones6050111

Cited by 4 publications

(3 citation statements)

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“…21 Görüntüler Kinovea 0.9.5 (www.kinovea.org) video analiz programına aktarılmış; 1280x720 pikselde ve 1 sn'de 120,01 kare sayısında (120 fps) olan görüntüyü ileri-geri alma, yavaşlatma, duraklatma özellikleri kullanılarak her bir yüzücünün bireysel olarak her 50 m için kulaç mekaniği ile ilgili tüm parametreleri hesaplanmıştır. 22 Kulaç mekaniği parametreleri birbirine matematiksel olarak bağlı olup, formüllerle hesaplanmaktadır. 23,24 Araştırma boyunca yapılan ölçümlerden önce yüzücülerin performansını olumlu/olumsuz etkileyecek antrenmanlardan kaçınılmıştır ve yüzücülerin ölçümlerden 2 saat öncesine kadar herhangi bir besin almamasına dikkat edilmiştir.…”

Section: Biyomekanik öLçümlerunclassified

Effects of Warm-up Performance on Stroke Mechanics in Swimming: Descriptive Research

ALTAN¹,

ODABAŞ²

2023

Turkiye Klinikleri J Sports Sci

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Section: Biyomekanik öLçümlerunclassified

Effects of Warm-up Performance on Stroke Mechanics in Swimming: Descriptive Research

ALTAN¹,

ODABAŞ²

2023

Turkiye Klinikleri J Sports Sci

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“…This limitation has spurred investigation into the use of unmanned aerial vehicles (UAVs) as a potential solution to overcome these challenges. UAVs have previously been utilized for diverse purposes, such as agricultural area surveys [30], disease assessments in vegetable and cultivated fruit crops [31], terrestrial studies [22], marine research [32], and even aquaculture [33]. However, the application of UAVs equipped with deep learning, particularly hybrid deep learning and machine learning (CNN combined with XGBoost), for assessing the weight of farm-raised fish in real-world conditions remains unexplored.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Convolutional Neural Networks, XGBoost, and Hybrid CNN-XGBoost for Precise Red Tilapia (Oreochromis niloticus Linn.) Weight Estimation in River Cage Culture with Aerial Imagery

Taparhudee,

Jongjaraunsuk,

Nimitkul

et al. 2024

AgriEngineering

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Accurate feeding management in aquaculture relies on assessing the average weight of aquatic animals during their growth stages. The traditional method involves using a labor-intensive approach and may impact the well-being of fish. The current research focuses on a unique way of estimating red tilapia’s weight in cage culture via a river, which employs unmanned aerial vehicle (UAV) and deep learning techniques. The described approach includes taking pictures by means of a UAV and then applying deep learning and machine learning algorithms to them, such as convolutional neural networks (CNNs), extreme gradient boosting (XGBoost), and a Hybrid CNN-XGBoost model. The results showed that the CNN model achieved its accuracy peak after 60 epochs, showing accuracy, precision, recall, and F1 score values of 0.748 ± 0.019, 0.750 ± 0.019, 0.740 ± 0.014, and 0.740 ± 0.019, respectively. The XGBoost reached its accuracy peak with 45 n_estimators, recording values of approximately 0.560 ± 0.000 for accuracy and 0.550 ± 0.000 for precision, recall, and F1. Regarding the Hybrid CNN-XGBoost model, it demonstrated its prediction accuracy using both 45 epochs and n_estimators. The accuracy value was around 0.760 ± 0.019, precision was 0.762 ± 0.019, recall was 0.754 ± 0.019, and F1 was 0.752 ± 0.019. The Hybrid CNN-XGBoost model demonstrated the highest accuracy compared to using standalone CNN and XGBoost models and could reduce the time required for weight estimation by around 11.81% compared to using the standalone CNN. Although the testing results may be lower than those from previous laboratory studies, this discrepancy is attributed to the real-world testing conditions in aquaculture settings, which involve uncontrollable factors. To enhance accuracy, we recommend increasing the sample size of images and extending the data collection period to cover one year. This approach allows for a comprehensive understanding of the seasonal effects on evaluation outcomes.

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“…This has spurred the use of unmanned aerial vehicles (UAVs) as a device to help reduce such problems. Previously, the use of aircraft has been applied for many purposes, such as surveying agricultural areas 23 , surveying diseases in vegetable and cultivated fruit crops 24 , terrestrial surveys 25 , marine surveys 26 (Fong et al, 2022), or even aquaculture 27 . However, the use of UAVs with an MVS for real-world applications in sh farming has not been reported.…”

Section: Introductionmentioning

confidence: 99%

Using unmanned aerial vehicle (UAV) with machine vision systems (MVS) to assess fish weight of red tilapia in river cage culture

Taparhudee,

Jongjaraunsuk,

Nimitkul

et al. 2023

Preprint

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Efficiently estimating fish weight poses a major challenge for effective fish feeding and harvesting.This study introduced a novel approach using an Unmanned Aerial Vehicle (UAV) and a Machine Vision System (MVS) to non-intrusively estimate the weight of red tilapia fish within a cultured environment (river-based cage culture). Our proposed method applied image acquisition via the UAV and subsequent image analysis using the MVS. Initially, Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Artificial Neural Network (ANN) models were trained for image classification across 9 fish classes in 5 cages. Subsequently, these models were tested on another set of 9 fish classes in 3 different cages. The results demonstrated that ANN achieved the highest prediction efficiency during training and validation, having mean (± standard deviation) scores for accuracy, precision, recall, and F1 of 90.39±8.33, 90.13±10.61, 90.05±9.14, and 90.13±9.05 %, respectively. However, during testing, the accuracy was 42 %. Therefore, future implementations are recommended to expand the dataset and to optimize ANN parameters (including K-fold, training cycle, hidden layers, and neurons) to enhance the efficiency of the ANN model. Where a substantial accuracy improvement cannot be achieved, we suggest considering the utilization of Convolutional Neural Network models and image data generator with transfer learning techniques.

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Using Drones to Assess Volitional Swimming Kinematics of Manta Ray Behaviors in the Wild

Cited by 4 publications

References 71 publications

Effects of Warm-up Performance on Stroke Mechanics in Swimming: Descriptive Research

Effects of Warm-up Performance on Stroke Mechanics in Swimming: Descriptive Research

Optimizing Convolutional Neural Networks, XGBoost, and Hybrid CNN-XGBoost for Precise Red Tilapia (Oreochromis niloticus Linn.) Weight Estimation in River Cage Culture with Aerial Imagery

Using unmanned aerial vehicle (UAV) with machine vision systems (MVS) to assess fish weight of red tilapia in river cage culture

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