## 1 Introduction
In recent years, AR technology and SG devices are being increasingly tested, especially in professional contexts, such as the industrial [12; 13], medical [14], educational and research sectors and many others like the agricultural sector [15; 16; 17], besides the SG producer companies, are more focused in developing solutions primarily for manufacturing and engineering field [18; 19; 20]. Interestingly, however, one of first widespread AR results was in the entertainment field with Pokemon Go smartphone application [21]. Nevertheless, there are usability problems related to human-machine interactions that can limit the spread of SG devices for AR technology [10; 22]. These are linked to low computational power and that the available applications are still basic [5], thus the final user may have problems with using the SG if proper applications related to his field of work are not available. Over the past decades, dairy farms have adopted innovative technologies to improve their productivity, profit and animal welfare [23; 24]. Modern milking systems allow recording of milk yields, milking times, electrical conductivity, milk flow rates and alarms for each animal [25; 26]. Currently, the level of technology available largely differs according to dairy-producing species. Dairy sheep farms, for instance, have the lowest technological potential, with a majority relying on conventional milking systems. Historical knowledge and individual productivity are fundamental for flock grouping [27], especially for feeding strategies [28; 29] and genetic improvement programs [30], which consist of identifying and grouping animals with predetermined characteristics (mainly milk yield and prolificacy). Moreover, these programs require a considerable amount of human, animal and material resources [31; 32]. The identification and grouping of animals for breeding purposes is often highly labor-intensive, commonly involving from two to three operators. It is normally performed in a milking parlor during the milking session. Therefore, the accessibility of real-time information (e.g., milk yield per year, number of live offspring per parturition, etc.) per single animal represents a valuable step forward for breeders. Consequently, linking the individual information of each animal to SG for AR may prove useful for dairy farmers, allowing the completion of work tasks with less workforce thanks to the free use of both hands [33]. To support the effective and safe agriculture adoption of smartglasses for augmented reality, there is a need to provide evidence-based results for SG use in on-farms activities. Additionally, the information type, such as colors and symbols used in a user interface may affects work performance [34]. However, the scientific literature to date fails to address the application of augmented reality in livestock farms. Previous research has highlighted the influence of SG design/category and data composition type on worker safety and performance [35; 36; 37] and on the obstacles crossing strategies adopted to the wearers to minimize the trip risks [38]. Moreover, the information disposition on the see-through display and walking/sitting behavior affect the text comprehension on the SG [39]. Nevertheless, more studies are needed to support the widespread diffusion of SG for AR in the agricultural and livestock field. The overall aim of this study is to extend the breadth of knowledge on the interaction of AR devices with a farming environment. Specifically, this work focuses on: (i) the feasibility and performances of animal identification for grouping activities using smartglasses for augmented reality; (ii) the human perception and the usability of smartglasses in on-farm activities. Moreover, the composition of the animal information (i.e., text vs. graphic) displayed through a commercially available SG was also evaluated.