AI and Data Analytics in the Dairy Farms: A Scoping Review

Osvaldo Palma ^1,2, Lluis M Plà-Aragonés ^1,3, Alejandro Mac Cawley ⁴

⁰Department of Mathematics, Universidad de Lleida, 73 Jaume II, 25001 Lleida, Spain.

Animals : an Open Access Journal From Mdpi +

|

May 14, 2025

View abstract on PubMed

Summary

Data analytics and artificial neural networks are key for predicting bovine milk production. Future dairy farm decision-making tools should integrate these technologies with simulation methods for improved efficiency.

Area Of Science

Agricultural Technology
Data Science
Animal Science

Background

Rising global population necessitates increased bovine milk production.
Advanced technologies like data analytics, big data, and sensors are crucial for efficient milk yield enhancement.
A review of modern technologies and data analytics for milk prediction is timely to support dairy farm decision-making.

Purpose Of The Study

To conduct a scoping review of modern technologies and data analytics methods for bovine milk production prediction.
To identify trends in research scope, data types, analytical methods, and predictive models.
To inform future research directions for enhanced dairy farm decision-making tools.

Main Methods

Scoping review methodology applied to 151 relevant articles.
Analysis of publication trends, study scope, data input types, and analytical techniques.
Evaluation of machine learning models, particularly artificial neural networks and convolutional neural networks.

Main Results

Studies are recent (average 5.95 years old), focusing on milk prediction (29%), lameness detection (26%), and mastitis detection (13%).
Predictive analytics dominate (87%), with limited use of prescriptive analytics (3%). Historical data (70%) is preferred over real-time data (25%).
Artificial neural networks (47%) and convolutional neural networks (24%) are prevalent, showing good accuracy but high variability. Simulation tools (4%) and stochastic models (5%) are underutilized.

Conclusions

Artificial neural networks offer significant potential for data analytics in dairy farming.
Integrating optimization-simulation methods with artificial neural networks can enhance future decision-making tools for bovine milk production.
Further research should explore the combined power of advanced analytics and simulation for dairy farm management.

Keywords:

data analytics machine learning milk production neural networks simulation