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Estrus detection in dairy cattle: an updated review on strategies and technologies
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Many factors may negatively affect dairy cattle productivity, among which imprecise estrus detection is a major contributor to low reproductive efficiency. Given that artificial insemination is widely used in the dairy industry, accurate estrus detection is of great importance. Estrus synchronization using hormonal treatments is a common approach to regulate the estrous cycle and to improve the efficiency of estrus detection. Estrus detection using artificial intelligence-aided data analyses is a new development. Biomarker-based chemical sensors and activity based wearable and implantable sensors are also available for monitoring. Behavioral datasets have been used in machine learning and other approaches; however, such datasets must be validated for their applicability in detecting estrus before being utilized by dairy farmers. The cumulative knowledge of estrus detection approaches, their merits, demerits, applicability, and cost-effectiveness is highly warranted. Therefore, here, we provide an update on the recent approaches and technologies used for estrus detection, particularly in dairy cattle, and discuss emerging experimental technologies with potential for future applications.
Title: Estrus detection in dairy cattle: an updated review on strategies and technologies
Description:
Many factors may negatively affect dairy cattle productivity, among which imprecise estrus detection is a major contributor to low reproductive efficiency.
Given that artificial insemination is widely used in the dairy industry, accurate estrus detection is of great importance.
Estrus synchronization using hormonal treatments is a common approach to regulate the estrous cycle and to improve the efficiency of estrus detection.
Estrus detection using artificial intelligence-aided data analyses is a new development.
Biomarker-based chemical sensors and activity based wearable and implantable sensors are also available for monitoring.
Behavioral datasets have been used in machine learning and other approaches; however, such datasets must be validated for their applicability in detecting estrus before being utilized by dairy farmers.
The cumulative knowledge of estrus detection approaches, their merits, demerits, applicability, and cost-effectiveness is highly warranted.
Therefore, here, we provide an update on the recent approaches and technologies used for estrus detection, particularly in dairy cattle, and discuss emerging experimental technologies with potential for future applications.
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