Intelligent wearable devices and biosensors for monitoring cattle health conditions: A review and classification

Alipio, Melchizedek I.; Villena, Maria Lorena

doi:10.1016/j.smhl.2022.100369

Cited by 27 publications

(9 citation statements)

References 58 publications

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“…First of all, the enteric CH4 emissions in ruminants can be monitored by doing direct or indirect measurements. The literature describing measurement methods and sensors systems for measuring CH4 production from ruminants is covering a wider interest nowadays and many implementation issues are discussed in [17]- [23]. Thus, according to the surveyed literature, a rough classification of the main available techniques can be provided according to following aspects:…”

Section: Methane Gas Emission Measurementmentioning

confidence: 99%

A measurement system for enteric CH4 emissions monitoring from ruminants in livestock farming

Verde,

Guerriero,

Bonavolonta

et al. 2023

Acta IMEKO

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In this paper, a proposal for an Internet-of-Things (IoT) based measurement system dealing with the enteric methane (CH4) emission monitoring from ruminants is presented. Herein, a brief overview of the recent advances in sensors technologies and their IoT integration for realizing measurement systems able to monitor the CH4 emissions in ruminants is also presented. Nowadays, it is confirmed that CH4 emissions, which are mainly produced during normal fermentation of feeds by the rumen microorganisms, are part of the Green-House Gas (GHG) emissions. Therefore, a classification of the existing measurement methods, sensing technologies and their impact on the animal’s welfare is presented. The proposed measurement system, together with its sensing elements and the developed data acquisition system are also reported in this paper. A preliminary disposal and field trials of the developed system in a farm facility is given.

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Section: Methane Gas Emission Measurementmentioning

confidence: 99%

A measurement system for enteric CH4 emissions monitoring from ruminants in livestock farming

Verde,

Guerriero,

Bonavolonta

et al. 2023

Acta IMEKO

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“…dos Santos et al, 2022), and estrus (Wang J et al, 2020), researchers have created MLbased algorithms. Individual cow health is continuously monitored by these ML-based systems using data from multiple sources, including sensors (Gertz et al, 2020), cameras (Wang et al, 2023), wearable (Alipio et al, 2022), and electronic health records (Mancuso et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Decoding Bovine Behavior: A Machine Learning Analysis of Disease and Event Detection

Nadeem,

Zubair,

Ali

et al. 2024

ABBDM

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The dairy industry globally serves millions, creates employment opportunities, and contributes to GDP and livelihoods, supporting numerous farmers and milk processors, and contributing to the GDP of many countries. Even with the high number of cows, a variety of illnesses such lameness, mastitis, metritis, foot and mouth disorders have a significant impact on the milk output. This demands the growth of smart methods to assist dairy producers in keeping an eye on the health, nutrition, and general well-being of their cows. To identify noteworthy bovine occurrences and illnesses, this research uses Machine Learning (ML) methods such as Extreme Gradient Boost (XGB), Naive Bayes (NB), and Perceptron. In order to uncover complex relationships and unearth undiscovered information about the characteristics and occurrences of bovine’s disease like Acidosis, Calving, Estrus, Lameness, and Mastitis. We closely scrutinize four distinct datasets. Metrics like as Area under the Curve (AUC), F1 score, accuracy, precision, recall, and Receiver Operating Characteristic (ROC) curve are used to evaluate performance. When it comes to identifying estrus events, XGB has the best detection accuracy score 92.59%, whereas XGB can detect a variety of events and disease with the highest recall 100% and the highest precision 95% and AUC of 0.962 when it comes to identifying calving, mastitis, and lameness

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“…They are primarily equipped with sensors that measure the temperature and activity based on motion obtained from accelerometers [ 9 ]. These data provide valuable information for animal behavior [ 10 ], health status [ 11 ], disease [ 12 ], stress [ 13 ], and estrus detection [ 14 ]. Radio frequency identification (RFID) tags are also used to obtain individuals’ identities.…”

Section: Introductionmentioning

confidence: 99%

Multiview Monitoring of Individual Cattle Behavior Based on Action Recognition in Closed Barns Using Deep Learning

Fuentes

Han

Nasir

et al. 2023

Animals

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Cattle behavior recognition is essential for monitoring their health and welfare. Existing techniques for behavior recognition in closed barns typically rely on direct observation to detect changes using wearable devices or surveillance cameras. While promising progress has been made in this field, monitoring individual cattle, especially those with similar visual characteristics, remains challenging due to numerous factors such as occlusion, scale variations, and pose changes. Accurate and consistent individual identification over time is therefore essential to overcome these challenges. To address this issue, this paper introduces an approach for multiview monitoring of individual cattle behavior based on action recognition using video data. The proposed system takes an image sequence as input and utilizes a detector to identify hierarchical actions categorized as part and individual actions. These regions of interest are then inputted into a tracking and identification mechanism, enabling the system to continuously track each individual in the scene and assign them a unique identification number. By implementing this approach, cattle behavior is continuously monitored, and statistical analysis is conducted to assess changes in behavior in the time domain. The effectiveness of the proposed framework is demonstrated through quantitative and qualitative experimental results obtained from our Hanwoo cattle video database. Overall, this study tackles the challenges encountered in real farm indoor scenarios, capturing spatiotemporal information and enabling automatic recognition of cattle behavior for precision livestock farming.

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Intelligent wearable devices and biosensors for monitoring cattle health conditions: A review and classification

Cited by 27 publications

References 58 publications

A measurement system for enteric CH4 emissions monitoring from ruminants in livestock farming

A measurement system for enteric CH4 emissions monitoring from ruminants in livestock farming

Decoding Bovine Behavior: A Machine Learning Analysis of Disease and Event Detection

Multiview Monitoring of Individual Cattle Behavior Based on Action Recognition in Closed Barns Using Deep Learning

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