IoT in agriculture: Development of novel sensor technology for online milk quality and cow health and welfare monitoring (PhD)

Optimization and intensification of livestock production are widely encouraged to meet the increasing demands for livestock products and to contribute to improving the livelihoods of rural households. To meet this demand, while at the same time keeping dairy farming sustainable and guaranteeing welfare for both the animal and the farmer, we require automation of the production process in combination with thorough health and welfare monitoring of the individual animals.

The research team “Livestock Technology” research team, under the lead of Prof. Ben Aernouts, develops, implements and validates innovative sensor technology and data processing algorithms to support animal management in livestock production. Our research lab is hosted by the KU Leuven campus in Geel, located in a green and rural environment, in close collaboration with research farms, industry and the livestock sector.
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Worldwide, dairy production is an important segment of agricultural production, representing about 10% of the total market value. Over the last 50 years, the average milk production per cow and lactation has increased enormously as a result of genetic selection and improved feed and management practices in dairy farming. However, due to the intense focus on high milk production, modern dairy cows are prone to production-related disorders. As the production of milk is a dominant factor in the metabolism of dairy cows, involving a very intense interaction with the blood circulation, the extracted milk contains valuable information on the metabolic status of the cow. Therefore, regular analysis of the produced milk is considered the most efficient way to monitor cow health.

Today, online measurements of milk production, conductivity and color are already common practice in dairy farming. Nevertheless, these milk parameters are influenced by many factors next to the health status of the cow. For this reason, there is a need for monitoring parameters that have a more direct link with the cow’s health. The formation of milk components, such as milk fat, protein and lactose, is the immediate result of the cow’s feed uptake and metabolic status, as well as the health of the milk-producing cells in the udder. As a result, regular analysis of the basic milk components for each individual cow can give valuable information on its udder health and metabolic and nutritional status.

In this project, an optical sensor prototype will be designed and built based on miniature spectrometers with the aim to measure milk quality. This sensor technology will be implemented in automatic milking systems to monitor the milk quality at the level of individual mammary glands. This will allow for the elimination of systemic effects of the animals through inter-quarter comparison, supporting a high-performance warning system for identifying mastitis. Furthermore, the robustness of the sensor will be evaluated and improved by applying different multivariate calibration strategies and machine learning approaches. Finally, the variation of the sensor measurements will be studied in relation to the cow’s health and combined with advanced data-processing techniques to obtain a robust early-warning system.

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For more information please contact Prof. dr. ir. Ben Aernouts, tel.: +32 14 72 13 64, mail: ben.aernouts@kuleuven.be.

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