Abstract
One of the main challenges in the adoption of artificial intelligence-based tools, such as integrated decision support systems, is the complexities of their application. This study aimed to define the relevant parameters that can be used as indicators for real-time detection of heat stress and subclinical mastitis in dairy cows. Moreover, this study aimed to demonstrate the use of a developed data-mining hub as an artificial intelligence-based tool that integrates the defined relevant information (parameters or traits) in accurately identifying the condition of the cow. A comprehensive theoretical framework of the data-mining hub is demonstrated, the selection of the parameters that were used for the data-mining hub is listed, and the relevance of the traits is discussed. The practical application of the data-mining hub has shown that using 21 parameters instead of 13 and 8 parameters resulted in a high overall accuracy of detecting heat stress and subclinical mastitis in dairy cows with a high precision effect reflecting a low percentage of misclassifying the conditions of the dairy cows. This study has developed an innovative approach in which combined information from different independent data was used to accurately detect the health and wellness status of the dairy cows. It can also be implied that an artificial intelligence-based tool such as the proposed theoretical data-mining hub of dairy cows could maximize the use of continuously generated and underutilized data in farms, thus ultimately simplifying repetitive and difficult decision-making tasks in dairy farming.
Highlights
Dairy farming is a decision-intensive field of agriculture that must rely on a holistic system approach accounting for the well-being, physiological, behavioral, and health conditions of the animal to meet the specific requirements of the dairy cows
The objectives of the study were (1) to demonstrate a comprehensive theoretical framework of the use of a data-mining hub in improving the precision of identifying the condition of dairy cows; (2) to provide a list of parameters or traits of the dairy cows that are relevant in detecting conditions of cows, and (3) to conduct a practical application to compare the number of traits or parameters needed to identify the statuses of the specific cow, which in this study, are identified as a normal condition, heat-stress, and subclinical mastitis risk (SCMR)
The set of equipment used by the farm in measuring parameters included the following: body weight measured by walk-over weighing system; body condition score (BCS) of the cows scored manually by farm staff; body temperature measured by thermosensors; rumen temperature, rumen pH, and rumination measured by rumen boluses; respiration rates measured by heart rate monitors, standing, lying, sitting, and drinking; behaviors of the cows measured by pedometers and surveillance video cameras
Summary
Dairy farming is a decision-intensive field of agriculture that must rely on a holistic system approach accounting for the well-being, physiological, behavioral, and health conditions of the animal to meet the specific requirements of the dairy cows. In automated health diagnostics, which are commercially and widely available [7,8], the movement, behavior, and physiological conditions of dairy cows are used to relay information about their well-being and health, such as MooMonitor [9] and Zigbee [10]. The use of these advanced monitoring and diagnostic tools, such as applications and expert systems, are normally restricted between the user and the proprietary of the tool, and these applications mostly focus on one problem and do not involve data integration [11]. Taking full advantage of the opportunities of the created data has been difficult for dairy farmers and animal scientists alike to accurately identify the condition of the cows for efficiently maximizing the productivity of the cows
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