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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 ...

The continuous rise in global population necessitates the prediction of food resources, among which milk stands out as one of the staples. Dairy farms and processing plants need to know how much milk they will have available to manage their storage and processing capacities efficiently. Overestimating production can lead to wastage, while underestimating can lead to shortages. Knowing the expected milk quantity helps in planning the transportation, processing, and distribution of milk and milk products. This study...

This paper focuses on recognizing the activity of dairy cows using a non-invasive approach that monitors four key behaviors: licking, feeding, standing, and lying. The study used IoT devices with accelerometers and gyroscopes attached to the cow's neck to continuously monitor its movements. The data collection process aimed to capture the dynamic and static nature of dairy cow behaviors, providing a valuable data set for subsequent analysis. To efficiently process the raw data, we analyzed it and then used long sh...