| dc.description.abstract | The rise of artificial intelligence (AI) is not going unnoticed in the agricultural
sector. The processing of the large amounts of data (’big data’) generated in
animal production is increasingly being done using artificial intelligence, particularly machine learning (ML). Machine learning is a branch of AI, in which
algorithms are automatically trained to solve a task of interest using a given
dataset. There are several sub-areas of ML, of which we focus on artificial neural
networks (ANNs), the most successfully used in agriculture. The basic units of
an ANN are artificial neurons. These are connected to each other similarly to synapses in the brain, forming a network. ANNs can be considered complex mathematical models that can make predictions from given data after a learning
process, taking into account millions of parameters. Because they are pretty
flexible, these networks have a wide range of applications in many fields. One
such field is a subset of agriculture, namely animal production. In our work, we
outline the general structure and operation of ANNs. We provide insight into the
metrics widely used to indicate the accuracy of prediction and their calculation
methods. Possible applications are illustrated with examples specifically from
the field of food production. The wide range of applications is illustrated by the
fact that the works cited also respond to the challenges faced by aquacultures
and beekeepers, in addition to the problems of cattle, pig and poultry farms.
Despite their many good features, ANNs cannot solve all problems, regardless
of type. Therefore, in our work we also concern about the limitations of the
method. Our work contributes to the definition of artificial intelligence, machine
learning, and artificial neural networks in the context of agriculture. | en_US |
