Predictive analytics of cattle behavior using machine learning techniques: A case study

Abstract

Livestock management is a critical aspect of agricultural sustainability and food security. Today, there is a pressing need for advanced tools in cattle behavior analysis to improve livestock welfare and productivity. We aimed to enhance cattle behavior classification by using accelerometers fitted in wearable collars. Deep learning techniques were employed to classify behavioral patterns in cattle such as feeding, moving, and lying. Ultimately, our study sought to improve livestock management practices, including the monitoring of health and overall well-being. The study was conducted in a local barn, where cattle were outfitted with specially designed collars with accelerometer sensors. These sensors recorded intricate movements, facilitating the collection of comprehensive behavioral data. Deep learning algorithms were used to process and analyze the accelerometer data, enabling precise classification of various behaviors exhibited by the cattle. Our results showed the effectiveness of AI-driven classification techniques in distinguishing cattle behaviors with a high degree of accuracy. Our findings underscore the potential of deep learning techniques in optimizing livestock management practices. This research significantly advances livestock management by offering a simple continuous monitoring solution for cattle behavior. Deep learning techniques not only enhance our understanding of cattle behavior but also pave the way for intelligent systems that empower farmers to make informed decisions. By promoting healthier and more productive livestock, this research contributes to the broader goal of enhancing global food security and sustainability in the livestock industry.