Papers - THI THI ZIN
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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.
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A Markov-Dependent stochastic approach to modeling lactation curves in dairy cows Reviewed International journal
Thi Thi Zin, Ye Htet, Tunn Cho Lwin, Pyke Tin
Smart Agricultural Technology 6 2023.12
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Smart Agricultural Technology
The modeling of lactation curves is an essential aspect of formulating farm managerial practices in dairy cows. In this study, we propose and examine a Markov-Dependent stochastic approach to modeling lactation curves in dairy cows, with the aim of developing a model that accurately fits lactation curves for a maximum number of lactations. Specifically, we develop a special type of Gamma Type Markov Chain Model that considers the first-order linear regressive property, which makes the model more realistic and reliable. We compared the proposed model with three other models - quadratic model, mixed log function, and wood model - using various goodness of fit measures such as adjusted R2, root mean square error (RMSE), and Bayesian Information Criteria (BIC). Our results showed that lactation curve modeling using the proposed model could help set management strategies at the farm level. However, it is important to optimize the modeling process regularly before implementing these strategies to enhance productivity in dairy cows. Our study contributes to the existing literature by proposing a novel approach that accounts for Markov dependence and linear regression in modeling lactation curves, which can lead to more accurate and reliable predictions. This modeling approach has practical implications for dairy farmers who seek to maximize productivity and efficiency while minimizing costs.
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Evaluating Imputation Strategies for Handling Missing Data: A Comparative Study Reviewed International journal
Tunn Cho Lwin, San Chain Tun, Pyke Tin, Thi Thi Zin
GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics 508 - 509 2023.10
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics
Missing data is a significant challenge across various domains of data analysis, impacting the accuracy of analysis and interpretation of underlying patterns and relationships within datasets. This study specifically focuses on two different datasets: addressing the absence of depth values in cattle backbone data captured using 3-D cameras and addressing missing data in real-time recordings of RR intervals in fetal health rate variability (FHRV) obtained from the sensors used for internal monitoring of electrocardiogram (ECG) recordings taken prior to fetal delivery. To tackle these gaps, popular time series imputation techniques, including linear interpolation, spline interpolation, and autoregressive models, are employed. The performance of each model is evaluated using the root mean square error (RMSE). This study ultimately selects the optimal model for handling the missing data which is important for data analysis research work.
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A Study on Assessment of Falling Risk in the Elderly Using a Balance Task Reviewed International journal
K. Kamahori, Thi Thi Zin
GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics 513 - 514 2023.10
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics
Due to the risk of serious injury or death resulting from falls among the elderly, it is crucial to detect and prevent falls proactively. Performing a fall risk assessment in advance allows healthcare professionals to identify individuals at an early stage who are at risk of falling. This paper presents a method for assessing the risk of falls in the elderly using a 3D camera during a balance task. By utilizing a 3D camera, the risk of falling can be evaluated irrespective of the patient's attire or how they are dressed. Additionally, the proposed balance task is relatively simple, resulting in a relatively low burden on the participants. Features are extracted from the recorded balance task video and analyzed using a classifier. The validation results demonstrate that this method achieved an accuracy of up to 76.5% in classification.
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A NON-INVASIVE METHOD FOR LAMENESS DETECTION IN DAIRY COWS USING RGB CAMERAS Reviewed International journal
T. Onizuka、Thi Thi Zin, I. Kobayashi
ICIC Express Letters, Part B: Applications 14 ( 10 ) 1107 - 1114 2023.10
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters, Part B: Applications
Lameness is a major health issue affecting dairy cows, causing pain, discomfort, and abnormal movements that can lead to decreased productivity and other diseases. Early detection and treatment are crucial to prevent the development of more serious conditions. In this paper, we present a method for detecting lameness in dairy cows using an RGB camera and analyzing their walking behavior. Our proposed technique achieves an accuracy of 84.6% in classifying cows as healthy or lame. We conducted a series of real-life experiments to validate our classification results, comparing them with expert diagnoses. Our method has the potential for use in routine farming conditions to detect lameness early and improve cow welfare and productivity.
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A Markov Chain Model for Determining the Optimal Time to Move Pregnant Cows to Individual Calving Pens Reviewed International journal
Cho Nilar Phyo, Pyke Tin, Thi Thi Zin
Sensors 23 ( 19 ) 2023.10
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Sensors
The use of individual calving pens in modern farming is widely recognized as a good practice for promoting good animal welfare during parturition. However, determining the optimal time to move a pregnant cow to a calving pen can be a management challenge. Moving cows too early may result in prolonged occupancy of the pen, while moving them too late may increase the risk of calving complications and production-related diseases. In this paper, a simple random walk type Markov Chain Model to predict the optimal time for moving periparturient cows to individual calving pens was proposed. Behavior changes such as lying time, standing time, and rumination time were analyzed using a video monitoring system, and we formulated these changes as the states of a Markov Chain with an absorbing barrier. The model showed that the first time entering an absorbing state was the optimal time for a pregnant cow to be moved to a calving pen. The proposed method was validated through a series of experiments in a real-life dairy farm, showing promising results with high accuracy.
DOI： 10.3390/s23198141
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Efficient Segment-Anything Model for Automatic Mask Region Extraction in Livestock Monitoring Reviewed International journal
Su Myat Noe, Thi Thi Zin, Pyke Tin, I. Kobyashi
IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 167 - 171 2023.9
Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin
This paper presents an efficient segment-anything model for automatic mask region extraction in livestock tracking. The research focuses on developing and evaluating automatic mask segmentation models for tracking black cattle. The primary contribution is a tailored extraction segmentation model for automatically extracting cattle mask regions utilizing in the livestock tracking. The methodology utilizes Segment Anything Model (SAM), Grounded SAM, Grounding Dino, YOLOv8, and DeepOCSort algorithms for detection and tracking. Experimental results demonstrate the effectiveness of the proposed approach in extracting black cattle mask regions and improving livestock tracking. Integration of YOLOv8 and DeepOCSort ensures accurate association and tracking of mask regions across frames. The findings advance livestock tracking, with applications in precision agriculture. The proposed segment-anything model serves as a valuable tool for automatic mask region extraction in foreground-background separation.
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Markov Chain Modelling for Heart Rate Variability Analysis: Bridging Artificial Intelligence and Physiological Data Reviewed International journal
Thi Thi Zin, Pyke Tin
IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 163 - 166 2023.9
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin
Heart rate variability (HRV) is a vital measure that captures the variations in beat-to-beat intervals. It is a significant indicator of autonomic nervous system activity and has been linked to various health conditions. Analyzing HRV involves processing complex physiological data with inherent uncertainties. Artificial Intelligence (AI) refers to the development of intelligent machines capable of mimicking human intelligence and learning. Markov Chain theory provides a powerful framework for studying and mathematically modelling situations with random variables. In this paper, we explore the application of Markov Chain concepts to HRV analysis, treating it as an AI problem. We represent beat-to-beat intervals as a sequence of random variables, forming Markov Chain states where the current state depends on the immediate previous state. We derive the state-to-state probability transition matrix and compute the stationary distribution probabilities. Using these probabilities, we calculate the Shannon entropy measure to gain insights into heart rate variability. Furthermore, we present the utilization of real-life experimental data to illustrate the effectiveness of the proposed method. The experimental results demonstrate the promising potential of the integration of Markov Chain and AI in analyzing HRV by achieving the highest accuracy of 95%. This research a novel perspective on understanding the underlying dynamics of HRV and its implications for human health.
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Temporal-Dependent Features Based Inter-Action Transition State Recognition for Eldercare System Reviewed International journal
Ye Htet, Thi Thi Zin, H. Tamura, K. Kondo, E. Chosa
IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 106 - 111 2023.9
Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin
Elderly individuals are particularly vulnerable to accidents, with a significant number of incidents occurring during transition states between primitive actions such as sitting to standing and sitting to lying. This paper introduces a novel machine-learning technique in artificial intelligence, based on temporal-dependent features to assist the elderly. To ensure privacy, we employed stereo depth cameras for data acquisition from the elder care center and exclusively processed depth images. The first step of our approach involves localizing individuals using the YOLOv5 object detector. Subsequently, we employed the Segment Anything Model to segment only the person masks, excluding other areas from consideration. Temporal-dependent features were then extracted for every five frames from the subsequent person masks that enable the recognition of transition states from primitive actions. We tested various classification approaches and compared the results by defining norms and metrics. Our experimental findings demonstrated that the overall accuracy rates for classifying 2 classes and 5 classes on small segments are 91.18% and 91.67% respectively. To validate the effectiveness of our proposed method, we conducted experiments using real-life environments inside three rooms and obtained average accuracy rates of 90.17%, 97.16%, and 77.44% respectively. Overall, this model has the potential to enhance the safety and well-being of the elderly population.
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A COMPARATIVE STUDY ON COW RECOGNITION: ANALYZING COLOUR SPACES, DISTANCE MEASURES AND DEEP NEURAL NETWORKS Reviewed International journal
Cho Cho Mar, Thi Thi Zin, Pyke Tin, K. Honkawa, I. Kobayashi, Y. Horii
ICIC Express Letters, Part B: Applications 14 ( 9 ) 993 - 1000 2023.9
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters, Part B: Applications
The implementation of autonomous cattle monitoring systems is becoming increasingly important in the livestock production and dairy farming industries. These systems require robust object detection and tracking systems to enhance their performance and reliability. The recognition task plays a crucial role in building a powerful tracking system. The aim of this study is to perform the recognition task for cow recognition by analyzing and comparing different colour spaces and distance measures to select the optimal ones. We extracted colour moment features and Co-occurrence Matrix (CM) features from various colour spaces: RGB, YCbCr, XYZ, HSV, CIELab, and grey level. We compared these features using different distance measures based on their accuracy values. We also conducted experiments on different pre-trained deep neural networks to extract Convolutional Neural Network (CNN) features and compared the accuracy values of the classification results with the Support Vector Machine (SVM) method. These experiments were conducted on the cow dataset created from a continuous 2-hour video. The results demonstrate that the combination of CM features extracted from the HSV colour space, and the Manhattan distance measure produced the highest accuracy values. Furthermore, we found that using the InceptionV3 pre-trained deep neural network produced the best accuracy results when combined with the SVM classifier. These findings provide insights into optimizing cow recognition for autonomous monitoring systems.
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PREDICTING DAIRY COW CALVING TIME USING MARKOV MONTE CARLO SIMULATION AND NAÏVE BAYES CLASSIFIER Reviewed
Thi Thi Zin, Swe Zar Maw, Pyke Tin, Y. Horii, H. Hama
ICIC Express Letters, Part B: Applications 14 ( 8 ) 877 - 888 2023.8
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters, Part B: Applications
This study proposes an approach that utilizes both Markov Monte Carlo simulation and the Bayesian method to predict the time of calving events in dairy cows. Continuous video surveillance was conducted on 30 individual dairy cows 24 hours before calving. Behaviors such as lying, transition from lying to standing, standing, and transition from standing to lying were annotated for each cow between 72 to 168 hours prior to calving. The probabilities for each behavior were derived and used in Markov Monte Carlo simulations to generate behavior patterns of each cow before calving. Three types of datasets, actual, simulated, and a mixture of the two, were investigated using Naïve Bayes Classifier for prediction. The experimental results showed that the hybrid approach accurately classified the calving event, cent by cent. This approach can assist farmers and veterinarians in making informed decisions and taking appropriate actions before the calving event, ultimately improving the health and welfare of dairy cows.
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Quantifying Body Movement Variability for Cattle Lameness Detection Using Imaging Reviewed International journal
Thi Thi Zin and Pyke Tin
ICIC Express Letters, Part B: Applications 14 ( 7 ) 735 - 74 2023.7
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters, Part B: Applications
This paper proposes a framework for detecting cattle lameness by quantifying the variability of body movement using depth imaging data collected while cows walk from the milking center to the resting area. The framework identifies critical factors that determine lameness scores based on the root mean square successive differences, various types of information entropies, and geometric measures of the collected depth data. To analyze lameness status, we developed an operational simulation model that combines Monte Carlo simulation with popular probability distribution functions such as uniform, normal, Poisson, and Gamma distributions. The simulation results suggest that detection performance and the characteristics of lame and non-lame cows significantly affect body movement variability. By using real-life data, we aim to validate this conjecture in future work.
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Artificial Intelligence Fusion in Digital Transformation Techniques for Lameness Detection in Dairy Cattle Reviewed
Thi Thi Zin, Ye Htet, San Chain Tun and Pyke Tin
International Journal of Biomedical Soft Computing and Human Sciences (IJBSCHS) 28 ( 1 ) 1 - 8 2023.7
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (scientific journal)
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Facial Region Analysis for Individual Identification of Cows and Feeding Time Estimation Reviewed International journal
Yusei Kawagoe, Ikuo Kobayashi, Thi Thi Zin
Agriculture (Switzerland) 13 ( 5 ) 2023.5
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (scientific journal)
With the increasing number of cows per farmer in Japan, an automatic cow monitoring system is being introduced. One important aspect of such a system is the ability to identify individual cows and estimate their feeding time. In this study, we propose a method for achieving this goal through facial region analysis. We used a YOLO detector to extract the cow head region from video images captured during feeding with the head region cropped as a face region image. The face region image was used for cow identification and transfer learning was employed for identification. In the context of cow identification, transfer learning can be used to train a pre-existing deep neural network to recognize individual cows based on their unique physical characteristics, such as their head shape, markings, or ear tags. To estimate the time of feeding, we divided the feeding area into vertical strips for each cow and established a horizontal line just above the feeding materials to determine whether a cow was feeding or not by using Hough transform techniques. We tested our method using real-life data from a large farm, and the experimental results showed promise in achieving our objectives. This approach has the potential to diagnose diseases and movement disorders in cows and could provide valuable insights for farmers.
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Facial Region Analysis for Individual Identification of Cows and Feeding Time Estimation Reviewed International journal
Y. Kawagoe, I. Kobayashi, Thi Thi Zin
Agriculture Switzerland 13 ( 5 ) 2023.5
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Agriculture Switzerland
With the increasing number of cows per farmer in Japan, an automatic cow monitoring system is being introduced. One important aspect of such a system is the ability to identify individual cows and estimate their feeding time. In this study, we propose a method for achieving this goal through facial region analysis. We used a YOLO detector to extract the cow head region from video images captured during feeding with the head region cropped as a face region image. The face region image was used for cow identification and transfer learning was employed for identification. In the context of cow identification, transfer learning can be used to train a pre-existing deep neural network to recognize individual cows based on their unique physical characteristics, such as their head shape, markings, or ear tags. To estimate the time of feeding, we divided the feeding area into vertical strips for each cow and established a horizontal line just above the feeding materials to determine whether a cow was feeding or not by using Hough transform techniques. We tested our method using real-life data from a large farm, and the experimental results showed promise in achieving our objectives. This approach has the potential to diagnose diseases and movement disorders in cows and could provide valuable insights for farmers.
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Validation and Discussion of Severity Evaluation and Disease Classification Using Tremor Video Reviewed International journal
T. Hayashida, T. Sugiyama, K. Sakai, N. Ishii, H. Mochizuki, Thi Thi Zin
Electronics (Switzerland) 12 ( 7 ) 2023.4
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Electronics (Switzerland)
A tremor is a significant symptom of Parkinson’s disease, but it can also be a characteristic of essential tremor, thereby hampering even specialists’ ability to differentiate between the two. This study proposes a system that leverages a single RGB camera to evaluate tremor severity and support the differential diagnosis of Parkinson’s disease and essential tremor. The system captures motor symptoms, performs time–frequency analysis using wavelet transforms, and classifies severity and disease using linear classification models. The results showed an accuracy rate of 0.56 for disease classification and 0.50 for severity classification (with an acceptable accuracy rate of 0.96). The analysis indicated that there was a low level of correlation between disease and each feature and a moderate correlation (about 0.6) between severity and each feature. Based on these results, this study recommends classifying severity with a linear model and disease with a nonlinear model to obtain improved accuracy.
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Comparing State-of-the-Art Deep Learning Algorithms for the Automated Detection and Tracking of Black Cattle Reviewed International journal
Su Myat Noe, Thi Thi Zin, Pyke Tin, I. Kobayashi
Sensors (Basel) 23 ( 1 ) 2023.1
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal)
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Cattle Face Detection with Ear Tags Using YOLOv5 Model Reviewed International journal
Wai Hnin Eaindrar Mg, Thi Thi Zin
ICIC Express Letters, Part B: Applications 14 ( 1 ) 65 - 72 2023.1
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters, Part B: Applications
With the increasing population’s need for meat and requirements for high food quality, the livestock industry is developing from small-scale and subsistence farming towards intensive and specialized grazing. Cattle monitoring and management system is crucial to be registered for breeding association, food quality tracing, disease prevention and control and fake insurance claims. This research presents cattle face detection with their ear tags’ names by applying light-weight YOLOv5 (You Only Look Once) model. This research is intent to the farmers who can not only monitor and manage the cattle conditions at the farm. The proposed system was trained to get the best accuracy model. The accuracy of the proposed model achieves up to 99.4% for four surveillance cameras.