Machine vision-based automatic lamb identification and drinking activity in a commercial farm.

Using ear tags, farmers can track specific data for individual lambs such as age, medical records, body condition scores, genetic abnormalities; to make data-based decisions. However, automatic reading of ear tags using Radio Frequency Identification requires (a) an antenna, (b) a reader, (c) comparable reading standards; consequently, such a system can be expensive and impractical for a large group of lambs, especially in situations where animals are not required to have a compulsory Electronic identification, contrary to the case in Europe, where it is mandatory. Therefore, this paper proposes a machine vision system for indoor animals to identify individual lambs using existing ear tags.

Short Communication: The effect of age on young sheep biometric identification.

Biometric identification provides an important tool for precision livestock farming. This study investigates the effect of weight gain and sheep maturation on recognition performance. Sheep facial identification was implemented using two convolutional neural network (CNN) called Faster R-CNN, and ResNet50V2, equipped with the state-of-art Additive Angular Margin (ArcFace) loss function.

A machine vision system to predict individual cow feed intake of different feeds in a cowshed.

Data on individual feed intake of dairy cows, an important variable for farm management, are currently unavailable in commercial dairies. A real-time machine vision system including models that are able to adapt to multiple types of feed was developed to predict individual feed intake of dairy cows. Using a Red-Green-Blue-Depth (RGBD) camera, images of feed piles of two different feed types (lactating cows' feed and heifers' feed) were acquired in a research dairy farm, for a range of feed weights under varied configurations and illuminations.