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Evaluation of Body Condition Score for Walking Dairy Cows Using 3D Camera 16th

Evaluation of Body Condition Score for Walking Dairy Cows Us...

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Chikunami, Masaya Zin, Thi Thi Aikawa, Masaru Kobayashi, Ikuo Graduate School of Engineering University of Miyazaki Miyazaki Japan Organization for Learning and Student Development University of Miyazaki Miyazaki Japan Sumiyoshi Livestock Science Station Field Science Center Faculty of Agriculture University of Miyazaki Miyazaki Japan

ISBN: (纸本)9789819615346

Body Condition Score (BCS) is an important index for assessing body fat accumulation in cattle and plays a crucial role in managing cattle productivity, feeding efficiency, and overall health. Currently, BCS evaluations predominantly rely on visual assessment and palpation by specialized personnel, which is time-consuming and labour-intensive. Consequently, many farms refrain from utilizing BCS for cattle management. Previous studies have focused on BCS evaluation of stationary dairy cows in rotary parlours, but this approach is not feasible for small and medium-sized livestock producers lacking such facilities. To enable BCS management for dairy cows on any farm, we propose a system utilizing image processing technology for evaluating cows while walking. In this study, we capture images using three 3D cameras and construct an estimation model using feature extraction and multiple regression analysis. This model allowed the evaluation of cows with large BCS within an error margin of 0.25. Our findings suggest that this approach can significantly streamline BCS evaluation, making it accessible and practical for a broader range of dairy farms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Livestock

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Automatic Body Temperature Detection in Calves and Alarm System Using Thermographic Camera 16th

Automatic Body Temperature Detection in Calves and Alarm Sys...

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Moe, Aung Si Thu Zin, Thi Thi Aikawa, Masaru Kobayashi, Ikuo Graduate School of Engineering Miyazaki Japan Organization for Learning and Student Development Miyazaki Japan Sumiyoshi Livestock Science Station Field Science Center Faculty of Agriculture University of Miyazaki 1-1 Gakuen Kibanadai-Nishi Miyazaki889-2192 Japan

ISBN: (纸本)9789819615308

The health monitoring of cows is crucial in livestock farming, particularly for calves, which are more susceptible to infectious diseases than adult cattle. This vulnerability is significantly influenced by the maturation of the calf’s immune system, rearing environment, and stress management. Traditional methods of health monitoring require substantial manpower, which can be impractical and inefficient. This paper proposes an automatic body temperature detection system for calves using thermal imaging. Leveraging the capabilities of thermal images, which are widely used in security, medical, and industrial applications, this system aims to identify and monitor the body temperature of calves. By detecting the head and eyes of the calf and extracting temperature data through the Detectron2 object detection method, the system can provide timely notifications to farmers or veterinarians. The overall average detection rate of head and eye regions was 94.5%. This approach enhances the efficiency of livestock health management, reducing the reliance on manual labor and enabling early detection of health issues. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： automated body temperature detection calves face detection Detectron2 thermal imaging system

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Cattle Lameness Detection Using Leg Region Keypoints from a Single RGB Camera 16th

Cattle Lameness Detection Using Leg Region Keypoints from a ...

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Myint, Bo Bo Zin, Thi Thi Aikawa, Masaru Kobayashi, Ikuo Tin, Pyke Graduate School of Engineering Miyazaki Japan Organization for Learning and Student Development Miyazaki Japan Sumiyoshi Livestock Science Station Field Science Center Faculty of Agriculture University of Miyazaki 1-1 Gakuen Kibanadai-Nishi Miyazaki889-2192 Japan

ISBN: (纸本)9789819615308

The recent rise of machine learning and deep learning has significantly impacted the field of computer vision, particularly in tasks like object detection, object tracking, and classification. This surge in interest has underscored the critical role of feature extraction as a foundational step in these machine learning pipelines. Our research focuses on applying feature extraction techniques to a cattle lameness dataset. We specifically extract features related to the movement of key points on cattle legs across a sequence of video frames. By analyzing the variations in these points, we aim to identify features that can efficiently differentiate between lame and no lame cattle using popular machine learning algorithms. All four classifiers achieved strong testing accuracy above 75%, with SVM excelling at over 84%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Object detection

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Cow’s Back Surface Segmentation of Point-Cloud Image Using PointNet++ for Individual Identification 16th

Cow’s Back Surface Segmentation of Point-Cloud Image Using ...

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Kyaw, Pyae Phyo Tin, Pyke Aikawa, Masaru Kobayashi, Ikuo Zin, Thi Thi Graduate School of Engineering Miyazaki Japan Organization for Learning and Student Development Miyazaki Japan Sumiyoshi Livestock Science Station Field Science Center Faculty of Agriculture University of Miyazaki 1-1 Gakuen Kibanadai-Nishi Miyazaki889-2192 Japan

ISBN: (纸本)9789819615308

An automatic cow health monitoring and management system cannot function effectively without an identification system in livestock farming. While 2D image-based computer vision currently achieves high accuracy in cow identification, its effectiveness can be significantly decreased by changes in lighting, environmental factors, and other limitations. To address these limitations, an identification system based on point-cloud images will be developed by using a combination of 3D TOF camera and 2D RGB camera. This system includes detection and segmentation stage, feature extraction stage, and identification stage. In this study, I focus on detecting and segmenting of cow back surface region from a point-cloud image using the PointNet+ + algorithm. Two segmentation models are trained and compared based on single-scale grouping (SSG) and multi-scale grouping (MSG) features. The extracted cow back surface region offers a rich set of features valuable for several applications, including individual cow identification, lameness detection, and body condition scoring. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Automated Cattle Identification via Image-Based Ear Tag Recognition 16th

Automated Cattle Identification via Image-Based Ear Tag Reco...

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Shimizu, Yuichiro Zin, Thi Thi Aikawa, Masaru Kobayashi, Ikuo Graduate School of Engineering University of Miyazaki Miyazaki Japan Organization for Learning and Student Development University of Miyazaki Miyazaki Japan Sumiyoshi Livestock Science Station Field Science Center Faculty of Agriculture University of Miyazaki Gakuen Kibanadai-Nishi 1-1 Miyazaki889-2192 Japan

ISBN: (纸本)9789819615308

A cattle management system using image processing technology has been proposed to reduce the labor burden of livestock farmers and improve management efficiency. To make cattle management using image processing more efficient, individual identification is necessary. Therefore, we focused on the 10-digit individual identification number given to each cow. In this study, face detection is first performed using YOLOv8, and ear regions are set from the detected face regions. The ear tag region is extracted from the ear region using color information, and the individual identification number on the ear tag is read. The identification result of the ear tag number may not be correctly identified due to the fact that a part of the ear tag is not captured due to the movement of the face or ear. Therefore, we proposed a mechanism to give a confidence score to the character identification results, update the ear tag number identification results, and obtain the ear tag number correctly, and confirmed its effectiveness through a demonstration experiment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image enhancement

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Identification of Rumination Patterns in Cattle Through Optical Flow Analysis and Machine learning Techniques 16th

Identification of Rumination Patterns in Cattle Through Opti...

引用

16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Ishikawa, Taichi Zin, Thi Thi Aikawa, Masaru Kobayashi, Ikuo Graduate School of Engineering University of Miyazaki Miyazaki Japan Organization for Learning and Student Development University of Miyazaki Miyazaki Japan Sumiyoshi Livestock Science Station Field Science Center Faculty of Agriculture University of Miyazaki Japan 1-1 Gakuen Kibanadai-Nishi Miyazaki889-2192 Japan

ISBN: (纸本)9789819615346

Rumination is a critical indicator of a cow’s physiological state, making it a valuable metric for managing cow health and predicting calving. Traditional human observation of rumination behaviour is time consuming and impractical for continuous monitoring, and sensor-based identification can be stressful for the cows due to the need for attachment. To address these challenges, this study introduces a non-contact method for identifying cow rumination. The proposed approach involves capturing video footage of multiple cows from above, recognizing cow regions, and validating the method’s effectiveness through experiments. Specifically, we utilize optical flow and frame-to-frame subtraction methods to extract moving cow regions from the recorded videos. From this data, we derive 11 features and employ a Support Vector Machine (SVM) for classification. Training the SVM with label features resulted in a test data identification accuracy of approximately 60%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optical flows

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Cattle Lameness Classification Using Cattle Back Depth Information 16th

Cattle Lameness Classification Using Cattle Back Depth Infor...

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Tun, San Chain Tin, Pyke Aikawa, Masaru Kobayashi, Ikuo Zin, Thi Thi Interdisciplinary Graduate School of Agriculture and Engineering University of Miyazaki Miyazaki Japan Graduate School of Engineering University of Miyazaki Miyazaki Japan Organization for Learning and Student Development University of Miyazaki Miyazaki Japan Field Science Center Faculty of Agriculture Sumiyoshi Livestock Science Station University of Miyazaki Miyazaki Japan

ISBN: (纸本)9789819615308

The livestock industry plays a crucial role in sustaining agricultural production and rural economies. Monitoring cattle health, however, presents significant challenges on large farms where traditional methods require diagnosing each animal individually. Lameness is a major issue affecting cattle health, leading to decreased production performance on many farms. Timely detection of lameness is essential for providing effective early treatment. In this study, we propose a system using specialized depth cameras to monitor and analyze cattle back information for classifying lameness scores. We employ Detectron2 for cattle detection and segmentation, and the Intersection over Union (IOU) method for tracking, focusing solely on the cattle’s depth region. We extract various features from the cattle’s back depth data and utilize three different machine learning algorithms: K-Nearest Neighbor (KNN), Gradient Boosting, and Extra Trees for lameness score classification. The models KNN, Gradient Boosting, and Extra Trees showed strong training and validation results. Testing showed Extra Trees performing well with 88.2% morning and 89.0% evening accuracy. Our approach demonstrates the potential of depth camera in effectively classifying lameness scores, offering significant implications for livestock health management. This method not only improves the efficiency and accuracy of health monitoring in large-scale farming but also provides a practical solution for early detection and treatment of lameness, thereby enhancing overall farm productivity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Depth Image Extra Trees Gradient Boosting K-Nearest Neighbor (KNN) Lameness

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A Study on Health Management by Behavior Analysis of Calves 16th

A Study on Health Management by Behavior Analysis of Calves

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Nishiyama, Taketo Kazuhisa, Shiiya Aikawa, Masaru Kobayashi, Ikuo Zin, Thi Thi Graduate School of Engineering University of Miyazaki 1-1 Gakuen Kibanadai-Nishi Miyazaki889-2192 Japan Organization for Learning and Student Development University of Miyazaki 1-1 Gakuen Kibanadai-Nishi Miyazaki889-2192 Japan Sumiyoshi Livestock Science Station Field Science Center Faculty of Agriculture University of Miyazaki 1-1 Gakuen Kibanadai-Nishi Miyazaki889-2192 Japan

ISBN: (纸本)9789819615346

It is important to always monitor the health of cattle, especially calves, and the frequency of observation increases with environmental changes in addition to once a day. In addition, calves tend to be more susceptible to infectious diseases because of their immature immune systems. Therefore, rearing management is extremely important. And the number of dairy cattle-keeping households and the total number of cattle are decreasing, while the number of cattle per household is increasing, indicating that management is becoming larger in scale. In this study, we proposed the development of a health management system by analyzing calf behavior using a 3D camera. Experiments were conducted at the Sumiyoshi Field of Miyazaki University to confirm the effectiveness of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Livestock

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Research on Individual Identification of Walking Cows Using a 3D Camera 16th

Research on Individual Identification of Walking Cows Using ...

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Shiihara, Yo Zin, Thi Thi Aikawa, Masaru Kobayashi, Ikuo Graduate School of Engineering University of Miyazaki 1-1 Gakuen Kibanadai-Nishi Miyazaki889-2192 Japan Organization for Learning and Student Development University of Miyazaki 1-1 Gakuen Kibanadai-Nishi Miyazaki889-2192 Japan Sumiyoshi Livestock Science Station Field Science Center Faculty of Agriculture University of Miyazaki 1-1 Gakuen Kibanadai-Nishi Miyazaki889-2192 Japan

ISBN: (纸本)9789819615346

This research focuses on identifying individual cows based on features from their back regions captured by 3D cameras, aiming to enhance management efficiency and reduce labor burdens. The methodology involves using 3D cameras to collect data on cows walking through a milking parlor. The captured data is processed to extract specific features such as pixel counts and volume from the cow’s back region. Various classification methods, including SVM, k-NN, decision trees, and random forests, are employed to identify individual cows. Experimental results demonstrated that the random forest classifier achieved the highest accuracy at 95%, outperforming other methods. The study highlights the limitations of current RFID-based systems, such as cost and stress on animals, and presents a non-contact alternative that reduces labor and improves accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Livestock

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Utilizing Behavioral Features for Predicting Calving Time 16th

Utilizing Behavioral Features for Predicting Calving Time

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Mg, Wai Hnin Eaindrar Tin, Pyke Aikawa, Masaru Kobayashi, Ikuo Horii, Yoichiro Honkawa, Kazuyuki Zin, Thi Thi Interdisciplinary Graduate School of Agriculture and Engineering University of Miyazaki Miyazaki Japan Graduate School of Engineering University of Miyazaki Miyazaki Japan Organization for Learning and Student Development University of Miyazaki Miyazaki Japan Field Science Center Faculty of Agriculture Sumiyoshi Livestock Science Station University of Miyazaki Miyazaki Japan Center for Animal Disease Control University of Miyazaki 1-1 Gakuen Kibanadai-Nishi Miyazaki889-2192 Japan Honkawa Ranch Oita877-0056 Japan

ISBN: (纸本)9789819615308

Accurately predicting calving time in cattle is crucial for enhancing livestock management and ensuring animal welfare. Our research presents a novel approach combining advanced computer vision and deep learning techniques to predict calving time based on cattle behavior. We employ a custom YOLOv8 model for cattle detection, achieving robust and efficient localization of individual cattle in various farm environments. Our Customized Tracking Algorithm (CTA) is utilized to maintain continuous identity tracking for each cow, allowing for precise monitoring of behavioral patterns. Feature extraction is performed using ResNet50, capturing detailed spatial characteristics of the detected cattle. These features are then organized into sequences to prepare them for temporal analysis. Finally, Custom Long Short-Term Memory (CLSTM) network is used for classification, leveraging the sequential nature of the data to predict the onset of calving with high accuracy. Our classification approach achieved an average accuracy of 94.88%. Our findings indicate promising performance from our CLSTM algorithm, accurately forecasting the remaining 3 h before calving. Through a comprehensive exploration of data collection, pre-processing, and feature engineering, our research paper establishes the foundation for training an accurate behavior model to predict calving time. Predicting calving using traditional, manual methods like observing breeding records and visual cues is complex and prone to errors, with even experts sometimes failing to make accurate predictions. Additionally, manual prediction becomes impractical and costly as farm size increases. Our automated system demonstrated a significant improvement in prediction accuracy, reducing false positives and providing timely alerts. Our proposed method demonstrates significant potential for improving the precision and reliability of calving time predictions, offering valuable insights for farm management and veterinary care. © The Aut

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