Papers - THI THI ZIN
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Predictive modeling of cattle calving time emphasizing abnormal and normal cases by using posture analysis Reviewed International journal
May Phyu Khin, Pyke Tin, Y. Horii, Thi Thi Zin
Scientific Reports 14 ( 1 ) 31871 2024.12
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Scientific Reports
Accurate calving time prediction plays a critical role in ensuring the well-being of both mother and calf during parturition. Challenges during the calving process, particularly in abnormal cases, often necessitate human intervention to prevent potentially fatal outcomes. This study proposes a novel system for automated prediction of normal and abnormal cattle calving cases based on posture analysis. By analyzing changes in posture and identifying specific posture types exhibited by cattle, the system aims to provide early warnings of impending calving events, enabling timely intervention and risk mitigation measures. Leveraging advanced computer vision techniques, particularly the Mask R-CNN from the Detectron2 detection and the YOLOv8-pose classification method known for their efficient training time and overall accuracy, the system analyzes the frequency of posture changes and key postures like sitting, standing, feeding, sitting with extended legs, and tail-raised to predict calving cases with high precision. We discovered that the “sitting with leg extended” posture is a crucial indicator for abnormal calving events. By incorporating this posture into the classification process, the system aims to achieve high accuracy in predicting both normal and abnormal calving timeframes. Additionally, the system differentiates between normal and abnormal calving patterns by analyzing posture sequences leading up to parturition, focusing on timeframes such as 30 min, 1 h, and 2 h pre-calving. This comprehensive analysis aids in identifying potential calving complications and enables the implementation of proactive management strategies. By offering insights into optimal methods for predicting specific postures and optimizing calving time management practices, this research contributes to the field of precision livestock farming, ultimately enhancing animal welfare and reducing calving-related risks.
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Unobtrusive Elderly Action Recognition with Transitions Using CNN-RNN Reviewed
Ye Htet, Thi Thi Zin, H. Tamura, K. Kondo, E. Chosa
Journal of Signal Processing 28 ( 6 ) 315 - 319 2024.11
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：信号処理学会
This study addresses the efficient recognition of elderly people's daily actions, emphasizing transition states, using privacy-preserving depth data and deep learning algorithms. Stereo-depth cameras collect data from an elder care center, ensuring privacy by capturing only depth information without revealing identifiable details. The research investigates spatial and temporal features in movement patterns by employing a Convolutional Neural Network (CNN) for transfer learning on segmented person image sequences to extract spatial features, while a Recurrent Neural Network (RNN) decoder extracts temporal features. The proposed study evaluated various CNN and RNN integrated architectures, assessing algorithmic performance on real-world data from three elderly participants. Experimental outcomes reveal the best model achieving 95% overall accuracy for all actions and an average accuracy of over 80% for classifying transition states. Beyond accuracy, comprehensive evaluation includes precision, recall, and F1-score, offering a thorough assessment of the developed algorithm's practical effectiveness on real-world data.
DOI： 10.2299/jsp.28.315
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A STUDY OF THE EARLY DETECTION OF OTITIS MEDIA IN CALVES WITH TWO TYPES OF CAMERAS Reviewed International journal
T. Nishiyama, K. Shiiya, M. Aikawa, I. Kobayashi, Thi Thi Zin
ICIC Express Letters, Part B: Applications 15 ( 11 ) 1183 - 1191 2024.11
Authorship：Last author Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters, Part B: Applications
Calves tend to be more susceptible to infections than adult cattle. This may be due to a less mature immune system, stress from the rearing environment, and other factors. Early detection of disease can help prevent deterioration and the spread of infection. Therefore, in this study, we proposed to investigate the early detection of mycoplasma otitis media in a non-contact manner using RGB and thermal imaging cameras. We then conducted experiments at the Sumiyoshi Field of the University of Miyazaki to confirm the effectiveness of the proposed method.
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A 3D CAMERA APPROACH TO EVALUATING BODY CONDITION SCORE IN WALKING DAIRY COWS Reviewed International journal
M. Chikunami, Thi Thi Zin, M. Aikawa, I. Kobayashi
ICIC Express Letters, Part B: Applications 15 ( 10 ) 1089 - 1097 2024.10
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters, Part B: Applications
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 laborintensive. Consequently, many farms refrain from utilizing BCS for cattle management. Previous studies have focused on BCS evaluation of stationary dairy cows in rotary parlors, 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 system, 3D cameras are employed to capture images, and an evaluation model is constructed using feature extraction and multiple regression analysis. This model allowed the evaluation of cows with large BCS within an error margin of 0.25.
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Fusion of Strategic Queueing Theory and AI for Smart City Telecommunication System Reviewed International journal
Thi Thi Zin, Aung Si Thu Moe, Cho Nilar Phyo, Pyke Tin
Proceedings - 2024 IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems, MASS 2024 653 - 657 2024.10
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：Proceedings - 2024 IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems, MASS 2024
This paper explores the innovative intersection of queueing theory and artificial intelligence (AI), addressing emerging challenges for Smart City Telecommunication System. We propose a novel approach that integrates queueing theory with AI methodologies, particularly artificial neural networks (ANNs), to optimize strategic parameters in Markov Decision Process (MDP) problems in communication systems. These parameters include server numbers, customer waiting times, queue lengths, and other critical metrics. Our fusion approach demonstrates high efficacy and indicates significant advancements in applying machine learning to complex queueing theory issues. The study underscores the practical applications of this integration across various domains. We provide real-world examples illustrating the strategic use of AI-enhanced queueing models in improving user experiences and optimizing system efficiencies. Our discussions cover the benefits of intelligent resource allocation, dynamic load balancing, and adaptive service time optimization. We extend our exploration through specific case studies demonstrating the efficacy of this integration in industries such as telecommunications, healthcare, and transportation. For instance, in telecommunications, AI-driven queue management systems can dynamically allocate bandwidth to ensure optimal service quality. In healthcare, queueing models enhanced by AI can improve patient flow and reduce wait times, potentially leading to better healthcare outcomes. In this paper, we investigate the application of deep learning models to queueing theory, specifically focusing on [mention specific problem or application, e.g., 'forecasting queue lengths in real-time systems']. We implement and evaluate several deep learning architectures to determine their effectiveness in modeling and predicting queue dynamics. The paper concludes by emphasizing the necessity for continued interdisciplinary research, encouraging collaboration between AI experts and queueing theorists. This synergy is essential for unlocking new potential and addressing the increasingly complex challenges posed by modern systems. By fostering such collaboration, we anticipate significant breakthroughs that will transform both fields, leading to more efficient, adaptive, and intelligent systems capable of meeting future demands across various sectors.
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Analyzing Parameter Patterns in YOLOv5-based Elderly Person Detection Across Variations of Data Reviewed International journal
Ye Htet, Thi Thi Zin, Pyke Tin, H. Tamura, K. Kondo, S. Watanabe, E. Chosa
Proceedings - 2024 IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems, MASS 2024 629 - 634 2024.10
Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：Proceedings - 2024 IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems, MASS 2024
This study investigates the impact of data variations on parameter patterns within a YOLOv5-based elderly person detection model. We explore how changes in camera settings and environmental factors influence the model's parameters. Our research questions focus on how these variations affect parameter patterns, model robustness, and generalizability. We aim to identify the parameters and layers of the model most susceptible to variations and develop strategies to improve the model's performance across various datasets. The experiment involves collecting data from eight elderly participants in real-life elder care facility settings. During data acquisition, only depth images are recorded using stereo-depth cameras to protect privacy. After that, we train individual YOLOv5 models for each dataset through hyperparameter tuning and transfer learning. Optimal hyperparameters and the sensitive convolutional layer of each model are then compared. Class Activation Maps (CAMs) are utilized to visualize the network's focus, followed by analysis of weight distributions and correlation to identify parameter patterns. The findings will provide valuable insights for improving elderly person detection models for smart health care and their robustness to real-world variations.
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AUTOMATED CATTLE DETECTION USING MASK R-CNN AND IOU-BASED TRACKING WITH A SINGLE SIDE-VIEW CAMERA Reviewed International journal
Bo Bo Myint, T. Onizuka, Pyke Tin, M. Aikawa, I. Kobayashi, Thi Thi Zin
International Journal of Innovative Computing, Information and Control 20 ( 5 ) 1439 - 1447 2024.10
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：International Journal of Innovative Computing, Information and Control
In precision livestock farming, the early detection of lameness in cattle is an extremely important aspect of effective breeding management. Timely identification of lameness not only facilitates prompt and cost-efficient treatment but also plays a crucial role in avoiding possible future diseases. This study emphasizes the significance of intelligent visual perception systems for lameness detection in dairy cattle, particularly in the lane between from Milking Parlor to Cattle Barn. To address the cattle lameness issue, we employ an advanced deep learning, and image processing technique, i.e., Mask R-CNN from Detectron2 to detect and identify cattle regions for feature extraction of lameness detection. On the other hand, cattle tracking using IoU is also an important part of data accumulation for lameness classification. The results of this study contribute to ongoing efforts in precision animal husbandry and demonstrate the potential of intelligent visual recognition systems for early lameness detection.
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Precision Livestock Tracking: Advancements in Black Cattle Monitoring for Sustainable Agriculture Reviewed
Su Myat Noe, Thi Thi Zin, Pyke Tin, I. Kobayashi
Journal of Signal Processing 28 ( 4 ) 179 - 182 2024.7
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：信号処理学会
Utilizing computer vision for animal behavior monitoring provides significant benefits by minimizing direct handling and capturing diverse traits through a single sensor. However, accurately identifying animals remains a challenge. To address this, this study introduces an innovative approach to monitor black cattle in dynamic agricultural environments to ensure their health welfare. By integrating advanced techniques like DETIC for automated labeling and YOLOv8 for real-time detection, the research emphasizes improving accuracy and robustness in tracking black cattle tracking within complex open ranch environments. Moreover, the customized ByteTrack model tailored for ranch scenarios significantly enhances cattle tracking across intricate landscapes. Achieving a mean Average Precision (mAP) of 0.901 and a Multi-Object Tracking Accuracy (MOTA) of average accuracy 92.185% of four videos, this approach appears to offer a viable resolution for conducting individual cattle behavior analysis experiments through the application of computer vision.
DOI： 10.2299/jsp.28.179
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Identifying Black Cow Actions Using Kalman Filter Velocity and Multi-Stage Classification Reviewed
Cho Cho Aye, Thi Thi Zin, M. Aikawa, I. Kobayashi
Journal of Signal Processing 28 ( 4 ) 183 - 186 2024.7
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：信号処理学会
This study proposes an advanced camera-based monitoring system for individual black cows in closed farms. By leveraging computer vision and deep learning, the system identifies five key cow actions: eating, drinking, sitting, standing, and walking. A multi-stage approach classifies actions first as static (eating, drinking, sitting, standing) or dynamic (walking) categories based on Kalman Filter velocity information. Further classification distinguishes among four static actions. A Convolutional Neural Network (CNN) refines especially for sitting and standing. On the other hand, cow head regions and specific zone locations help distinguish eating and drinking. The system achieves an overall accuracy of 80% in long data sequences, demonstrating its potential for precision livestock farming.
DOI： 10.2299/jsp.28.183
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Development of a real-time cattle lameness detection system using a single side-view camera Reviewed International journal
Bo Bo Myint, T. Onizuka, Pyke Tin, M. Aikawa, I. Kobayashi, Thi Thi Zin
Scientific reports 14 ( 1 ) 13734 2024.6
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Scientific Reports
Recent advancements in machine learning and deep learning have revolutionized various computer vision applications, including object detection, tracking, and classification. This research investigates the application of deep learning for cattle lameness detection in dairy farming. Our study employs image processing techniques and deep learning methods for cattle detection, tracking, and lameness classification. We utilize two powerful object detection algorithms: Mask-RCNN from Detectron2 and the popular YOLOv8. Their performance is compared to identify the most effective approach for this application. Bounding boxes are drawn around detected cattle to assign unique local IDs, enabling individual tracking and isolation throughout the video sequence. Additionally, mask regions generated by the chosen detection algorithm provide valuable data for feature extraction, which is crucial for subsequent lameness classification. The extracted cattle mask region values serve as the basis for feature extraction, capturing relevant information indicative of lameness. These features, combined with the local IDs assigned during tracking, are used to compute a lameness score for each cattle. We explore the efficacy of various established machine learning algorithms, such as Support Vector Machines (SVM), AdaBoost and so on, in analyzing the extracted lameness features. Evaluation of the proposed system was conducted across three key domains: detection, tracking, and lameness classification. Notably, the detection module employing Detectron2 achieved an impressive accuracy of 98.98%. Similarly, the tracking module attained a high accuracy of 99.50%. In lameness classification, AdaBoost emerged as the most effective algorithm, yielding the highest overall average accuracy (77.9%). Other established machine learning algorithms, including Decision Trees (DT), Support Vector Machines (SVM), and Random Forests, also demonstrated promising performance (DT: 75.32%, SVM: 75.20%, Random Forest: 74.9%). The presented approach demonstrates the successful implementation for cattle lameness detection. The proposed system has the potential to revolutionize dairy farm management by enabling early lameness detection and facilitating effective monitoring of cattle health. Our findings contribute valuable insights into the application of advanced computer vision methods for livestock health management.
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Enhancing Fetal Heart Rate Monitoring Through Digital Twin Technology Reviewed International journal
Tunn Cho Lwin, Thi Thi Zin, Pyke Tin, T. Ikenoue, E. Kino
2024 IEEE Gaming, Entertainment, and Media Conference, GEM 2024 2024.6
Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：2024 IEEE Gaming, Entertainment, and Media Conference, GEM 2024
In the era of digital twin technology (DTT), which involves creating virtual replicas of physical systems, healthcare applications have seen a surge in innovation. Fetal heart rate monitoring, a rapidly advancing field within healthcare, is benefiting greatly from the effective implementation of DTTs. Digital twin, supported by Artificial Intelligence (AI), Virtual Reality (VR), and Extended Virtuality (EV), have already demonstrated significant impact in entertainment, gaming, and media sectors. This paper explores and analyzes fetal heart rate monitoring systems during labor using digital twin technologies. During labor, there is an elevated risk of developing non-communicable diseases (NCDs) such as diabetes, cardiovascular diseases, cancer, and chronic respiratory diseases. The developmental origins of health and disease hypothesis posits that environmental conditions during fetal and early postnatal development have enduring effects on growth, structure, and metabolism, ultimately influencing long-term health and well-being. Understanding and effectively monitoring fetal heart rate dynamics during labor using digital twin technologies can provide valuable insights into maternal and fetal health. The findings underscore the value of entropy analysis in FHRV assessment, presenting a pioneering predictor of fetal health via ECG data. Moreover, it reveals the current landscape of fetal heart rate monitoring, discusses the integration of digital twins, and proposes future directions for optimizing healthcare outcomes in the digital era.
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A Stochastics Branching Process Model for Analyzing Rumor Spreading in Social Media Networks Reviewed International journal
Thi Thi Zin, Pyke Tin, H. Hama
2024 IEEE Gaming, Entertainment, and Media Conference, GEM 2024 2024.6
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：2024 IEEE Gaming, Entertainment, and Media Conference, GEM 2024
This paper introduces a stochastics branching process model as an analytical tool to study the dynamics of rumor spreading in social media networks. Employing a specialized first-order conditional probability generating function method, we derive a second-order linear regressive equation to characterize the intricate dynamics of rumor propagation. The validation of our model's accuracy is established through a comparative analysis between numerical computations and Monte Carlo simulations. Additionally, we present a derived threshold condition for the spread control factor, also known as the reproduction rate. Numerical simulation results demonstrate that the interactions and responses within social platforms contribute to the rapid onset of rumors while simultaneously diminishing the maximum density of spreaders and the overall scale of the rumors. To provide further insights, we explore analogies between rumor-spreading models and epidemic models. In conclusion, our proposed stochastics branching process model not only enhances our understanding of rumor spreading in social media networks but also offers a valuable framework for investigating the interplay between various factors influencing the dynamics of information diffusion.
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A Markovian Game Theoretic Framework for Analysing a Queueing System with Multiple Servers Reviewed International journal
Pyke Tin, Thi Thi Zin
2024 IEEE Gaming, Entertainment, and Media Conference, GEM 2024 2024.6
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：2024 IEEE Gaming, Entertainment, and Media Conference, GEM 2024
This paper introduces a Markov game theoretic framework designed to analyze a Markovian queueing system equipped with multiple servers. Our focus lies in modelling a message transmission system, where messages traverse various transmission options, each associated with a cost and governed by a decision process. The primary objective is to investigate the impact of cooperation and communication, or their absence, among servers. The inherent uncertainty regarding the characteristics of the available transmission alternatives is mathematically captured through a Markovian game formulation. Within this framework, we quantify the inefficiency resulting from the self-interested management of individual servers and the associated loss attributed to the decision-making process. Our analysis encompasses diverse scenarios of signaling exchange among servers, providing valuable insights into the system's behavior under varying conditions.
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Smarter Aging: Developing a Foundational Elderly Activity Monitoring System With AI and GUI Interface Reviewed International journal
Ye Htet, Thi Thi Zin, Pyke Tin, H. Tamura, K. Kondo, S. Watanabe, E. Chosa
IEEE Access 12 74499 - 74523 2024.5
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：IEEE Access
The global rise in the elderly population, which presents challenges to healthcare systems owing to labor shortages in caregiving facilities, necessitates innovative solutions for elderly care services. Smart aging technologies such as robotic companions and digital home gadgets, offer a solution to these challenges by improving the elderly's quality of life and assisting caregivers. However, limitations in data privacy, real-time processing, and reliability often hinder the effectiveness of the existing technologies. Among these, privacy concerns are a major barrier to ensuring user trust and ethical implementation. Therefore, this study proposes a more effective approach for smart aging through elderly activity monitoring that prioritizes data privacy. The proposed system utilizes stereo depth cameras to monitor the activities of the elderly. Data were collected from real-world environments with the participation of six elderly individuals from a care center and hospital. This system focuses on recognizing common daily actions of the elderly including sitting, standing, lying down, and seated in a wheelchair. Additionally, it recognizes transition states (in-between actions such as changing from sitting to standing) that are crucial for assessing balance issues. By integrating motion information with a deep-learning architecture, the system achieved a high accuracy of 99.42% in recognizing daily actions in real-time. This high accuracy was maintained even with minimal data from new environments through transfer learning, and the adaptability of this model ensured its potential for real-world applications. For intuitive interaction between the caregivers and the system, a user-friendly graphical interface (GUI) was also designed in the proposed approach.
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Revolutionizing Cow Welfare Monitoring: A Novel Top-View Perspective with Depth Camera-Based Lameness Classification Reviewed International journal
San Chain Tun, T. Onizuka, Pyke Tin, M. Aikawa, I. Kobayashi, Thi Thi Zin
Journal of Imaging 10 ( 3 ) 2024.3
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Journal of Imaging
This study innovates livestock health management, utilizing a top-view depth camera for accurate cow lameness detection, classification, and precise segmentation through integration with a 3D depth camera and deep learning, distinguishing it from 2D systems. It underscores the importance of early lameness detection in cattle and focuses on extracting depth data from the cow’s body, with a specific emphasis on the back region’s maximum value. Precise cow detection and tracking are achieved through the Detectron2 framework and Intersection Over Union (IOU) techniques. Across a three-day testing period, with observations conducted twice daily with varying cow populations (ranging from 56 to 64 cows per day), the study consistently achieves an impressive average detection accuracy of 99.94%. Tracking accuracy remains at 99.92% over the same observation period. Subsequently, the research extracts the cow’s depth region using binary mask images derived from detection results and original depth images. Feature extraction generates a feature vector based on maximum height measurements from the cow’s backbone area. This feature vector is utilized for classification, evaluating three classifiers: Random Forest (RF), K-Nearest Neighbor (KNN), and Decision Tree (DT). The study highlights the potential of top-view depth video cameras for accurate cow lameness detection and classification, with significant implications for livestock health management.
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DIFFUSION-BASED INPAINTING METHODS COMPARISON WITH DAMAGE AREA REDUCTION TECHNIQUES Reviewed International coauthorship International journal
Khant Khant Win Tint, Mie Mie Tin, Thi Thi Zin, Pyke Tin
ICIC Express Letters, Part B: Applications 15 ( 3 ) 303 - 309 2024.3
Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters, Part B: Applications
Ancient murals beautifully reflect the social and religious characteristics of several cultural groups in a particular historical era. Unfortunately, the irreplaceable historical murals have been damaged by both natural and human-made deterioration. Image inpainting can restore the visual appeal of a mural. Image inpainting involves repairing any damaged or missing regions. In this paper, in order to address the issue of color bias, the gray scale image undergoes an inpainting process, resulting in a lack of noticeable color differences. For the mask generation, mask is generated automatically by using thresholding. That is why it prevents over-identifying damage or missing regions by user interaction. Experiments are conducted on mural images of Po-Win-Daung, Myanmar. To assess the inpainted results without the presence of a ground truth image, the paper puts forward the idea of using the damage area reduction technique for evaluation purposes. Comparisons are carried out on directional median diffusion and coherent transport methods.
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Customized Tracking Algorithm for Robust Cattle Detection and Tracking in Occlusion Environments Reviewed International journal
Wai Hnin Eaindrar Mg, Pyke Tin, M. Aikawa, I. Kobayashi, Y. Horii, K. Honkawa, Thi Thi Zin
Sensors 24 ( 4 ) 2024.2
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Sensors
Ensuring precise calving time prediction necessitates the adoption of an automatic and precisely accurate cattle tracking system. Nowadays, cattle tracking can be challenging due to the complexity of their environment and the potential for missed or false detections. Most existing deep-learning tracking algorithms face challenges when dealing with track-ID switch cases caused by cattle occlusion. To address these concerns, the proposed research endeavors to create an automatic cattle detection and tracking system by leveraging the remarkable capabilities of Detectron2 while embedding tailored modifications to make it even more effective and efficient for a variety of applications. Additionally, the study conducts a comprehensive comparison of eight distinct deep-learning tracking algorithms, with the objective of identifying the most optimal algorithm for achieving precise and efficient individual cattle tracking. This research focuses on tackling occlusion conditions and track-ID increment cases for miss detection. Through a comparison of various tracking algorithms, we discovered that Detectron2, coupled with our customized tracking algorithm (CTA), achieves 99% in detecting and tracking individual cows for handling occlusion challenges. Our algorithm stands out by successfully overcoming the challenges of miss detection and occlusion problems, making it highly reliable even during extended periods in a crowded calving pen.
DOI： 10.3390/s24041181
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Digital Transformation (DX) Solution for Monitoring Mycoplasma Infectious Disease in Calves: A Worldwide Health Challenge Reviewed International journal
Cho Nilar Phyo, Pyke Tin, H. Hama, Thi Thi Zin
Lecture Notes in Electrical Engineering 1114 LNEE 218 - 226 2024.1
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：Lecture Notes in Electrical Engineering
The Mycoplasma bovis (M. bovis) is a serious threat to cattle health, resulting in significant economic losses worldwide, particularly in veal calf sector. While the disease can circulate undetected, early identification of subclinical carriers is crucial. To this end, a fully automated monitoring system for Mycoplasma Infectious Disease in Calves was proposed using digital transformation technologies and AI advances. The proposed system will consist of four stages. In the first stage, an image processing technique will be developed to automatically or manually record behavioral or physiological parameters in calves while feeding at milk feeding robots. The second stage will integrate multiple data resources, such as DX records and image data, to analyze the data for detection and diagnosis of mycoplasma infection. The third stage will employ DX and AI advances to enforce the proposed monitoring system for making accurate decisions, such as whether to treat or not and what to treat calves for. In fourth stage, some experimental results will be displayed. In conclusion, the proposed automated monitoring system will provide a valuable tool for early detection of Mycoplasma Infectious Disease in calves, leading to reduce economic losses and offer timely information to address major worldwide problem.
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AI Driven Movement Rate Variability Analysis Around the Time of Calving Events in Cattle Reviewed International journal
Wai Hnin Eaindrar Mg, Pyke Tin, M. Aikawa, I. Kobayashi, Y. Horii, K. Honkawa, Thi Thi Zin
Lecture Notes in Electrical Engineering 1114 LNEE 227 - 237 2024.1
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：Lecture Notes in Electrical Engineering
In modern cattle management, the timely detection of cattle events is crucial for ensuring both animal welfare and farm profitability. This paper introduces an innovative approach that leverages AI-driven movement rate variability analysis to predict calving events in cattle. By harnessing advanced motion tracking technologies and machine learning algorithms, this methodology offers a non-intrusive and automated means of detecting physiological and behavioral changes associated with impending calving events. Through a comprehensive exploration of data collection, pre-processing, and feature engineering, this paper establishes the foundation for training accurate AI models. These models utilize distinct movement patterns, including changes in speed, frequency, direction, and rest behavior, as predictive indicators of calving events. Real-world validation on cattle farms underscores the practical viability of the proposed approach, demonstrating its potential to revolutionize calving event detection. By transcending traditional methods, this AI-driven solution exhibits superior accuracy and efficiency, thereby contributing to enhanced animal care, optimized farm operations, and improved economic outcomes. The paper concludes by highlighting future research avenues and underscoring the transformative implications of AI-driven movement analysis for calving event prediction in the realm of agricultural technology.
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Cow detection and tracking system utilizing multi-feature tracking algorithm Reviewed International journal
Cho Cho Mar, Thi Thi Zin, Pyke Tin, K. Honkawa, I. Kobayashi, Y. Horii
Scientific Reports 13 ( 1 ) 17423 2023.12
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Scientific Reports
In modern cattle farm management systems, video-based monitoring has become important in analyzing the high-level behavior of cattle for monitoring their health and predicting calving for providing timely assistance. Conventionally, sensors have been used for detecting and tracking their activities. As the body-attached sensors cause stress, video cameras can be used as an alternative. However, identifying and tracking individual cattle can be difficult, especially for black and brown varieties that are so similar in appearance. Therefore, we propose a new method of using video cameras for recognizing cattle and tracking their whereabouts. In our approach, we applied a combination of deep learning and image processing techniques to build a robust system. The proposed system processes images in separate stages, namely data pre-processing, cow detection, and cow tracking. Cow detection is performed using a popular instance segmentation network. In the cow tracking stage, for successively associating each cow with the corresponding one in the next frame, we employed the following three features: cow location, appearance features, as well as recent features of the cow region. In doing so, we simply exploited the distance between two gravity center locations of the cow regions. As color and texture suitably define the appearance of an object, we analyze the most appropriate color space to extract color moment features and use a Co-occurrence Matrix (CM) for textural representation. Deep features are extracted from recent cow images using a Convolutional Neural Network (CNN features) and are also jointly applied in the tracking process to boost system performance. We also proposed a robust Multiple Object Tracking (MOT) algorithm for cow tracking by employing multiple features from the cow region. The experimental results proved that our proposed system could handle the problems of MOT and produce reliable performance.