Scholarly record
ANALYSIS OF DAIRY COW BEHAVIORAL PATTERNS USING A COMBINATION OF IOT DATA AND SIGNAL PROCESSING TECHNIQUES
Abstract
This article presents a study focused on animal activity recognition using a combination of IoT devices and signal processing techniques. The study involves collecting data from IoT devices placed on the cow's neck, which are equipped with an accelerometer and gyroscope, along with a synchronized video camera. The objective is to accurately recognize and classify four key activities exhibited by the cow. To prepare the collected signals for analysis, various signal processing techniques are applied. This includes essential pre-processing steps to clean the data, such as noise removal and filtering, ensuring reliable and accurate activity recognition. Additionally, feature extraction processes are performed to enhance the accuracy and precision of behavioral models. The study also examines the boundaries and allowable variations for each specific cow movement. Furthermore, dimensionality reduction techniques are applied to reduce the complexity of the data. This study aims to develop an approach to analyze the behavior patterns of cows using IoT devices. The results contribute not only to our understanding of cow behavior but also hold potential implications for livestock management, health monitoring, and precision agriculture. This research paves the way for further exploration and development in the field of animal behavior studies, ultimately leading to improved welfare and productivity in livestock management practices.
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