Invited review: Big Data in precision dairy farming

Abstract

Insight into current scientific applications of Big Data in the precision dairy farming area may help us to understand the inflated expectations around Big Data. The objective of this invited review paper is to give that scientific background and determine whether Big Data has overcome the peak of inflated expectations. A conceptual model was created, and a literature search in Scopus resulted in 1442 scientific peer reviewed papers. After thorough screening on relevance and classification by the authors, 142 papers remained for further analysis. The area of precision dairy farming (with classes in the primary chain (dairy farm, feed, breed, health, food, retail, consumer) and levels for object of interest (animal, farm, network)), the Big Data-V area (with categories on Volume, Velocity, Variety and other V’s) and the data analytics area (with categories in analysis methods (supervised learning, unsupervised learning, semi-supervised classification, reinforcement learning) and data characteristics (time-series, streaming, sequence, graph, spatial, multimedia)) were analysed. The animal sublevel, with 83% of the papers, exceeds the farm sublevel and network sublevel. Within the animal sublevel, topics within the dairy farm level prevailed with 58% over the health level (33%). Within the Big Data category, the Volume category was most favoured with 59% of the papers, followed by 37% of papers that included the Variety category. None of the papers included the Velocity category. Supervised learning, representing 87% of the papers, exceeds unsupervised learning (12%). Within supervised learning, 64% of the papers dealt with classification issues and exceeds the regression methods (36%). Time-series were used in 61% of the papers and were mostly dealing with animal-based farm data. Multimedia data appeared in a greater number of recent papers. Based on these results, it can be concluded that Big Data is a relevant topic of research within the precision dairy farming area, but that the full potential of Big Data in this precision dairy farming area is not utilised yet. However, the present authors expect the full potential of Big Data, within the precision dairy farming area, will be reached when multiple Big Data characteristics (Volume, Variety and other V’s) and sources (animal, groups, farms and chain parts) are used simultaneously, adding value to operational and strategic decision.

Symposium review: Precision technologies for dairy calves and management applications

“…Typically, PDT establish a behavioral baseline on an individual level, and deviations from normal behavior trigger an alert to producers (Lokhorst et al, 2019). Changes in behavior often occur for reasons that need human intervention (e.g., estrus; Dolecheck et al, 2015) or illness (Stangaferro et al, 2016).…”

Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms

“…Most sensors or sensor systems currently aim to fit one specific purpose, implying that they are manufacturer-specific, and as such, encourage vendor lock-in. Linking different data sources from different farms would offer the most promising potential to develop better algorithms that could improve farm management [ 102 ]. Unfortunately, the data are difficult to integrate with additional data sources as companies restrict access to their records [ 103 ].…”

Animal Welfare Management in a Digital World

“…Moreover, it is rarely (if at all) aligned with other on-farm data sets, such as data from automatic milking systems, veterinary surveillance data or post-mortem data from slaughterhouses. The full potential of more integrated data fields is still a long way off [ 69 ], although many food chain actors are beginning to develop integrated data management systems across their supply chains for quality assurance purposes, aimed both at consumers and contracted producers. Examples of this include De Hoeve Innovatie (KDV) in the Netherlands and Carrefour in France who use sophisticated tracking technologies for their animal products.…”