Architecture principles for vision-based applications in agriculture : Initial step towards standardization

Abstract

In the last few years there is an increase in technology developments in agriculture using machine vision and deep learning to recognize specific plants or animals. A shared infrastructure to exchange image datasets and to support the workflow of image processing with neural networks could fasten up the developments of new vision-based applications in agriculture. This requires some form of standardization and architecture principles. The use case of plant specific weeding with robots is used to define an initial architecture for this infrastructure and to describe an initial set of preferred metadata for standardizing the exchange of image datasets and deep learning algorithms.