What question did this study set out to answer?

The aim is to investigate the role of AI and computer vision in improving livestock management and sustainability.

March 26, 2026Open Access

Computer vision in precision livestock farming: AI-driven technologies and applications for sustainable animal production

Key Points

The aim is to investigate the role of AI and computer vision in improving livestock management and sustainability.
Review of advancements in Precision Livestock Farming technologies and applications.
Analysis of data acquisition through visual and multi-modal sensors.
Evaluation of data analytics techniques using machine learning and deep learning.
Discussion of decision-support mechanisms for farm management interventions.
PLF enhances individual animal monitoring, contributing to early disease detection.
AI applications improve resource utilization and animal welfare standards.
Current challenges include high costs, data interoperability issues, and limited model generalizability.

Abstract

The accelerating global demand for animal-derived food products is placing unprecedented pressure on livestock production systems to increase efficiency while simultaneously ensuring animal welfare, environmental sustainability, and and economic viability.Precision Llivestock Ffarming (PLF) has emerged as a transformative paradigm that integrates advanced sensing technologies, computer vision, Internet of Tthings (IoT) infrastructures, and and Artificial Iintelligence (AI) to enable continuous, automated, and and individualized animal monitoring.This paper explores the evolution of livestock management from conventional observation-based practices to sophisticated, data-driven architecture.It also synthesizes recent advancements in Precision Livestock Farming (PLF), emphasizing its system architecture, key applications in cattle production, cross-sector expansion, and and emerging challenges.The core architecture of PLF is structured into three functional layers: (i) data acquisition through visual environmental sensors; data acquisition through multi-modal sensors, with a primary emphasis in this review on visual and environmental monitoring system; (ii) data analytics employing machine learning and deep learning techniques to establish behavioral and physiological baselines; and (iii) decision-support mechanisms that translate analytics into actionable farm management interventions.Major applications, including individual animal identification, body condition score estimation, lameness detection, calving time prediction, and and AI-powered health monitoring, are critically discussed.The extension of PLF principles to aquaculture and other livestock sectors is also examined.By transitioning from herd-level to individual-animal management, PLF offers a scalable, noninvasive strategy for early disease detection, optimized resource utilization, improved welfare standards, and and long-term economic sustainability.The current limitations, including high capital investment, data interoperability challenges, and and model generalizability constraints, have been analyzed, and and future research directions emphasizing explainable AI and welfare-oriented system design have

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Cite This Study

Zin et al. (Tue,) studied this question.

synapsesocial.com/papers/69c4cd8dfdc3bde44891a0cc https://doi.org/https://doi.org/10.5713/ab.260165

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