Artificial Intelligence in Support of Welfare Monitoring of Dairy Cattle: A Systematic Literature Review

Context: Although agribusiness corresponded to more than 20% of Brazil’s Gross Domestic Product (GDP), most livestock is under manual control and manual monitoring. Additionally, alternative technologies are either uncomfortable and stressful, or expensive. Now, despite the great scientific advances in the area, there is still a pressing need for an automated robust, inexpensive and (sub)optimal technology to monitor animal behavior in a cost-effective, contact-less and stress-free fashion. Overall, this niche can leverage the benefits of Deep Learning schemes.Objective: This review aims to provide a systematic overview of most current projects in the area of comfort monitoring dairy cattle, as well as their corresponding image recognition-based techniques and technologies.Methods: First, a systematic review planning was carried out, and objectives, research questions, search strings, among others, were defined. Subsequently,a broad survey was conducted to extract, analyze and compile the data, to generate a easy-to-read visual source of information (tables and graphics).Results: Information was extracted from the reviewed papers. Among this data collected from the papers are techniques utilized, target behaviors, cow bodyparts identified in visual computational, besides their paper source font, the publication date, and localization. For example, the papers present are mostly recent. China has had a larger number of relevant papers in the area. The back was the body region most analyzed by the papers and the behaviors most analyzed were body condition score, lameness, cow’s body position and feeding/drinking behavior. Among the methods used is RCNN Inception V3 with the best accuracy for cow’s back region.Conclusion: The aim of this work is to present some of the papers that are being carried out in the area of dairy cow behavior monitoring, using techniques of Artifical Intelligence. It is expected that the information collected and presented in the present systematic review paper contribute to the future researches and projects of the area and the application of new techniques.