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International Conference on Intelligent Networking and Collaborative Systems

INCoS 2021: Advances in Intelligent Networking and Collaborative Systems pp 42–48Cite as

Artificial Intelligence-Based Early Prediction Techniques in Agri-Tech Domain

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 312))

Abstract

This work is aimed at presenting an application of Artificial Intelligence techniques in the Precise Livestock Farming domain.

In particular, we focused on the possibility of identifying mastitis using non-invasive IR techniques, delegating to artificial intelligence techniques the task of early detecting ongoing mastitis situations.

We conduct an experimental campaign finalized to verify that an infrared thermography measurement technique (which absorbs infrared radiation and generates images based on the amount of heat generated) can be profitably used in the early detection of mastitis in buffalo populations.

For this experimental campaign, the surface temperature of the udder skin was measured using an infrared camera. We use a Deep Learning approach to perform an early classification of the infrared pictures, finalized to distinguish healthy breasts from those with ongoing mastitis symptoms.

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Author information

Authors and Affiliations

  1. Centro Servizi Metrologici e Tecnologici Avanzati (CeSMA), University of Federico II, Naples, Italy

    Alessandra Amato, Flora Amato, Leopoldo Angrisani & Francesco Bonavolontà

  2. Department of Electrical Engineering and Information Technologies, University of Federico II, Naples, Italy

    Flora Amato, Leopoldo Angrisani & Francesco Bonavolontà

  3. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  4. Department of Veterinary Medicine and Animal Productions, University of Federico II, Naples, Italy

    Gianluca Neglia & Oscar Tamburis

Authors
  1. Alessandra Amato
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  2. Flora Amato
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  3. Leopoldo Angrisani
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  4. Leonard Barolli
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  5. Francesco Bonavolontà
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  6. Gianluca Neglia
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  7. Oscar Tamburis
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Corresponding author

Correspondence to Flora Amato .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan

    Hsing-Chung Chen

  3. School of Science and Technology, Kwansei Gakuin University, Sanda, Japan

    Hiroyoshi Miwa

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Cite this paper

Amato, A. et al. (2022). Artificial Intelligence-Based Early Prediction Techniques in Agri-Tech Domain. In: Barolli, L., Chen, HC., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2021. Lecture Notes in Networks and Systems, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-030-84910-8_5

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