3D technology helps dairy farmers identify sick cows

Published on
November 13, 2019

Dairy farmers need to identify sick cows as quickly as possible so that can give them the right treatment in time. To help farmers do this, PhD student Xiangyu Song has developed an automated 3D system for monitoring dairy cows. He will obtain his PhD on Monday 18 November at Wageningen University & Research (WUR).

The system uses 3D cameras to monitor cows for bodily changes and rumen movements. Movements of the reticulum, body size and the Body Condition Score (BCS) are all automatically monitored, and deviations are recorded.

Important indicators

These physical characteristics can provide farmers with essential information for monitoring the daily health and nutritional management of the herd. The process is automated, non-invasive and welfare-friendly and therefore suitable for widespread use on the commercial dairy farms of the future.

The system uses 3D cameras to monitor cows for bodily changes and rumen movements.
The system uses 3D cameras to monitor cows for bodily changes and rumen movements.

'The system worked successfully in the experiments; I compared the automated measurements with expert assessments and the differences were negligible,' says Song. 'The 3D system was tested on a commercial dairy farm, monitoring individual cows for more than two months. We discovered that the condition of the animals responded quickly to the change from silage to fresh grass. The changes in rumen movements were successfully recorded by the 3D system. Changes in the BCS also confirmed the change of diet, but these followed many days later.'

Precision livestock farming

The new 3D system is a form of Precision Livestock Farming (PLF). This more accurate method of farming is booming due to the advent of new sensor technologies. The data generated by these technologies helps farmers to make informed decisions and they can use the systems to make their businesses more economically, socially and environmentally sustainable.

However, current PLF applications for managing dairy cow health are often slow in identifying diseases. The system developed in this PhD research enables farmers to monitor the biological processes more closely. This allows them to identify the development of metabolic abnormalities more quickly and to intervene proactively before a problem with the livestock materialises as a disease.

PhD details

Xiangyu Song: The Skin-Deep Beauty of Dairy Cows. Investigation of Metabolic Disorders by using Morphological Traits Quantified with 3-Dimensional Vision. The supervisor is Prof. P.W.G. Groot Koerkamp and the co-supervisors are Dr E.A.M. Bokkers and Dr P.P.J. van der Tol.

The PhD ceremony will be held on Monday, 18 November at 1.30 p.m. in the WUR Aula: