Modernizing Livestock Operations: Smart Feedlot Technologies and Their Impact

Area of Science:

• Agricultural Science
• Animal Science
• Engineering

Background:

• Smart feedlots increasingly use Precision Livestock Farming (PLF) for individual animal monitoring in beef production.
• This review synthesizes evidence on digital infrastructure, sensing technologies, and AI for proactive management.

Purpose of the Study:

• To review the current state and future directions of PLF technologies in smart feedlots.
• To identify key components, reported outcomes, and barriers to adoption.

Main Methods:

• Narrative review of ~350 academic publications (117 cited), industry deployments, and expert experience.
• Analysis of digital infrastructure, animal identification/sensing, machine vision, and AI analytics.

Main Results:

• PLF technologies are progressing from proof-of-concept to commercial application.
• Outcomes include reduced manual observation, earlier disease detection, and standardized welfare auditing.
• Vision-based and automated weighing methods show high accuracy for health and growth monitoring.
• Precision feeding improves feed utilization, with variable adoption.

Conclusions:

• Key priorities for scalable deployment include resilient communication, edge-cloud strategies, and multi-sensor data fusion.
• Persistent barriers include cost, durability, integration, data governance, and workforce skills.