Abstract
Improving existing animal husbandry practices is essential before introducing grazing animals to vineyards. In order to provide this type of assistance, it is necessary to monitor and condition the animals’ whereabouts and actions, especially their feeding posture. Using this strategy, sheep could graze in agricultural areas (such vineyards and orchards) without fear of harming them. Based on these findings, we have created an IoT-based platform for tracking animal habits. To facilitate unattended shepherding of ovine within vineyard areas, the system integrates a local Internet of Things network for data collection from the animals with a cloud platform with data dispensationalso storage competences. As a result, the system can tend to ovine flocks. Easy analysis and interpretation of Internet of Things (IoT) data is made possible by the machine learning capabilities built into the cloud platform. Therefore, we shall not only outline the platform but also supply some machine learning platform-specific results. To be more specific, testing looked at how well this platform could identify and characterize disorders related to animal posture. This page offers a comparison of the tested approaches because multiple algorithms were used.
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