The exchange of sustainability information in the supply chain is becoming increasingly important. Relevant attributes are animal welfare, the environment and other issues that are important for the consumer and the buyer. Wageningen Economic Research contributed to the measurement and exchange of sustainability information through the pork chain, in collaboration with HAS Den Bosch, ZLTO and the Vion Food Group. In this trial, this was concretely elaborated for the carbon footprint of pork. The project was carried out in the framework of a Public-Private Partnership project called DATA-FAIR, which investigates and innovates methods for data exchange in food chains.

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.

Sharing insights Conversation types Conversation tools Conversation purposes Building blocks Home Building Blocks Types Purposes ToolsA hands-on navigator to explore why, when and how to engage with dialogue in research for more impact in society. Home True listening, true understandingIn Theory U, Learning from the future as it emerges, Otto Scharmer (MIT) elaborates on the need for people to listen to each other. He points out the methods to develop the appropriate skills and attitude to do so. True listening, and true understanding, is a gateway to transformative change. Also for science. Home

This report discusses the importance of precision agriculture in achieving sustainability goals and the need for a basis that considers different perspectives of a data space such as interoperability, scalability, security, transparency, and data ownership. The Towards Precision Agriculture 4.0 project aims to address these perspectives to provide better-informed management decisions for farmers and the ecosystem. The current study focuses on determining minimum interoperability mechanisms concerning the standardization of image data and deep learning algorithms for vision-based applications in weed management by robots. The study adopts a metadata-oriented approach to make data and algorithms semantically interoperable and reuses existing knowledge from the Reference Model Agro (rmAgro). The results indicate the need for a balance between established standardization and agile standardization for supporting semantic interoperability, and the interoperability of preferred standards like Robot Operating System (ROS) and Open Neural Network Exchange (ONNX) is insufficient. The study results are useful for professionals and academia who work in the design and development of software for the farming business.