To counteract heat stress in dairy cows, a more reliable and efficient method for monitoring the activity of dairy cows and ambient environmental conditions should be developed. This research presents a cost-effective embedded imaging system that is capable of monitoring the drinking behaviour of dairy cows, while ambient temperature and humidity are simultaneously and continuously recorded with the integrated sensor modules. The embedded imaging system was implemented and tested on an experimental dairy farm, with imaging modules installed above the drinking troughs to collect video streams. To estimate the drinking time and frequency of dairy cows, detections of the dairy cow's head over the drinking troughs was performed on video stream using a deep learning convolutional neural network (CNN) model. The F1 score and true positive rate of the cow head detection were 0.987 and 0.983, respectively. The drinking behaviour data and environmental conditions were recorded and analysed to further assess the effects of heat stress on the drinking behaviour of dairy cows. The experimental results show that the daily total length and frequency of drinking bouts of the dairy cows were highly related to the temperature and humidity index (THI). Data from long-term monitoring using the automated imaging system clearly demonstrated that drinking behaviour reflects the effects of heat stress on dairy cows. The proposed monitoring system offers a novel approach for automatic and quantitative assessment of the drinking behaviours of dairy cows.