Digital Goat Farmer's Aid

Designing intelligence in interaction

Abstract

In livestock agriculture, earmarks are widely used to identify individual animals. Goat farmers are legally obligated to attach two earmarks to the goats' ears within the first six months of their lives. These earmarks have embedded identification numbers, which are used to report the goats' residence and migration information to the Dutch government's "Identificatie & Registratiesysteem (I&R)" (RVO, n.d.). Several problems are surrounding the earmarking of goats. A part of the problem focuses on animal welfare. Next to this, the business standard proves to be time- and labor-intensive. Lastly, the regulations for goat identification are sharpened, meaning that the farmer is at risk of receiving costly fines. However, no alternative is easily accessible. During this project, we explore the viability of the Digital Goat Farmer's Aid (DGFA), a computer vision-based goat identification as an alternative to earmarks.

To create a goat identification system, a normalized dataset of facial pictures of goats is required. As such a dataset is not publicly available, we created a dataset of goats ourselves. This dataset was preprocessed, after which features were extracted using Matlab. This processed data was used in a multilayer perceptron neural network to train a goat identification system using Neuroph Studio. Various types of data was extracted and compared with each other to get the best result.

The research resulted in an 83.3% predicted accuracy proving the feasibility of goat identification. This is not robust enough for implementation in the agricultural industry, but it does show that the identification of goats via image recognition is feasible. Discussion of future approaches towards face recognition on goats suggested the implementation of a deep learning convolutional neural network to better define specific head features of individual goats.

Responsibilities

Collaborating with goat farmer and potential stakeholders

Development business opportunity

Pre-processing data

Feature extraction

Development multilayer perceptron neural network

Identification of goats based on facial features

Identification of goats based on facial features

Identification of goats based on facial features

Identification of goats based on facial features

Identification of goats based on facial features

Data collection

References

I&R wordt strenger | Geitenhouderij. (2019). Retrieved from https://www.vakbladgeitenhouderij.nl/ir-wordt-strenger/

RVO. (2015, July 15) Oormerken schapen en geiten. Retrieved from https://www.rvo.nl/onderwerpen/agrarisch-ondernemen/dieren/dieren-registreren/schapen-en-geiten/oormerken-voor-schapen-en-geiten

© 2020 Jesper van Bentum