Development of an Anomaly Detection System for Cattle Using Infrared Image and Machine Learning

In Japan, the number of cattle is on a declining trend. There is a strong demand for a technology that can efficiently grasp the individual health status of cattle and detect changes in deep body temperature, which is the most basic and important tool for managing the health of cattle. In this study, we developed an artificial intelligence (AI) characterization module that estimates the deep body temperature in a noncontact manner using AI technology. A laboratory model was developed, and the coefficient of determination (R2) and the estimation accuracy were improved after machine learning, which shows the feasibility of the noncontact estimation of cattle body temperature. On the other hand, we collected the environment temperature, humidity, illuminance, and infrared (IR) images of the body surface of the cattle for the first time in a real-life environment of cattle to develop an AI characterization module that estimates the body temperature of cattle. By using this system to test three cattle, R2 = 0.287 was obtained after machine learning, and the estimation accuracy reached about ± 0.5 °C. This shows that the temperature inside cattle can be inferred to some extent, and the health status of cattle can be predicted from this temperature.

• Publication year

  2020

• Venue

  Sensors and materials

• Publication date

  2020-12-16

• Fields of study

  Agricultural and Food Sciences, Computer Science

• Identifiers
  DOI 10.18494/sam.2020.2913
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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