Integrating Artificial Intelligence in dairy farm management − biometric facial recognition for cows

The integration of Artificial Intelligence (AI) into dairy farm management through biometric facial recognition of cows marks a significant milestone in livestock care. This comprehensive review explores the development, implementation, and challenges associated with AI-powered biometric facial identification in dairy agriculture. It emphasizes the pivotal role of this innovation in enabling precise monitoring of individual cows, thereby facilitating thorough tracking of their health, behaviors, and productivity levels. Derived from facial recognition technologies originally designed for humans, this approach harnesses distinctive features of cow faces for gentle and immediate observation within large-scale farming operations. The evolution of AI from basic pattern recognition to advanced Convolutional Neural Networks (CNNs) and deep learning frameworks signifies a transition toward data-driven agriculture. This analysis addresses notable challenges such as environmental variability, data collection difficulties, ethical considerations, and technological limitations. Furthermore, it compares various AI frameworks, highlighting their unique advantages and suitability in the dairy farming context. Despite these obstacles, facial recognition technology holds promise for enhancing farm efficiency, improving animal welfare, and promoting sustainable practices, underscoring the need for ongoing research and innovation. We advocate for future investigations focused on enhancing adaptability to diverse environments, ensuring ethical AI deployment, fostering compatibility across different breeds, and integrating with complementary agricultural technologies. Ultimately, this review underscores the transformative impact of AI in advancing dairy farming towards a data-centric future while prioritizing responsible agricultural practices.

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Publication Details

Journal
Information Processing in Agriculture
Published
2024-10-09
DOI
https://doi.org/10.1016/j.inpa.2024.10.001
Citations
22
Primary Topic
Food Supply Chain Traceability
Type
article
Field-Weighted Citation Impact
7.67
Controls
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article

Integrating Artificial Intelligence in dairy farm management − biometric facial recognition for cows

Suresh Neethirajan, Shubhangi Mahato
22 citations
Information Processing in Agriculture
Food Supply Chain Traceability
7.67
article

Integrating Artificial Intelligence in dairy farm management − biometric facial recognition for cows

Suresh Neethirajan, Shubhangi Mahato
article en
22 citations

Abstract

The integration of Artificial Intelligence (AI) into dairy farm management through biometric facial recognition of cows marks a significant milestone in livestock care. This comprehensive review explores the development, implementation, and challenges associated with AI-powered biometric facial identification in dairy agriculture. It emphasizes the pivotal role of this innovation in enabling precise monitoring of individual cows, thereby facilitating thorough tracking of their health, behaviors, and productivity levels. Derived from facial recognition technologies originally designed for humans, this approach harnesses distinctive features of cow faces for gentle and immediate observation within large-scale farming operations. The evolution of AI from basic pattern recognition to advanced Convolutional Neural Networks (CNNs) and deep learning frameworks signifies a transition toward data-driven agriculture. This analysis addresses notable challenges such as environmental variability, data collection difficulties, ethical considerations, and technological limitations. Furthermore, it compares various AI frameworks, highlighting their unique advantages and suitability in the dairy farming context. Despite these obstacles, facial recognition technology holds promise for enhancing farm efficiency, improving animal welfare, and promoting sustainable practices, underscoring the need for ongoing research and innovation. We advocate for future investigations focused on enhancing adaptability to diverse environments, ensuring ethical AI deployment, fostering compatibility across different breeds, and integrating with complementary agricultural technologies. Ultimately, this review underscores the transformative impact of AI in advancing dairy farming towards a data-centric future while prioritizing responsible agricultural practices.

Information Processing in AgricultureVol. 12(3)
Dalhousie University (CA)
Zero hunger
Openalex Percentile: Top 2%
Food Supply Chain Traceability
7.67
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