Search

Agricultural production is a key economic activity providing a growing world population withresources like food, fibers, fuels, andchemicalbasic components. Yet, agriculture also plays acrucial role in the depletion of resources likefresh water, fossil fuels, and nutrients (e.g., phosphate) and has a significant impact on our living environmentthrough emissions and the use of chemicals. Despite a growing world population, labor participation in agricultural production is declining. Technology hasbeen instrumental in the very successful development of agriculture throughout the centuries and will play acrucial role in the European Green Deal that addresses the transition to a more sustainable agri-food productionin the years that lay ahead of us. It is expected that technologies like Artificial Intelligence, Data, and Robotics willbe leading in this transition. During this transition, it is critically important to support agri-food production withnovel technologies. Covering the value chain from breeding, through on-farm production, post-harvest storage,logistics, packaging, and food processing, the agri-food production network is an ecosystem providing agri-foodproducts and services. Besides food, other off-farm productions do exist including harvesting of wild food, fisheries, indoor farming, both large scale commercial, and home growing, forestry, and, last but not least, urban landscaping and maintenance where robot technologies can and will be applied as well. The current release of this agenda mainly focuses on the food production part. Being a working document, theauthors wish toexpress the hope that it will serve as a basis for further elaboration to encompass the whole agrifood domain in its full widthanddepth. With an emphasis on robotics and to a lesser extent on AI and Data, thisreport provides a vision and mission on what robotics should contribute and how to facilitate the transition to amore sustainable agri-food production. Key challenges in terms of technology, ecosystem, and market are derived from 12 use-case themes within theagri-food production domain. Use-case themes represent classes of functionality that go beyond specific individual applications and application domains. Despite of aiming to identify high-level cross-domain challenges in robotics, AI, and Data agri-food and offering the opportunity to connect to other technical and application domains,this classification is not considered to be exhaustive. &nbsp

In Kaljaca, Vroegindeweij, and van Henten (2020), there is a change in the author group, name of one of the author, Angelo Mencarelli, was accidentally removed from the author list and needs to be added back. This article was published in Journal of Field Robotics in February 2020 (https://doi.org/10.1002/rob.21917). We apologize for the error. Updated author group is: Dejan Kaljaca, Bastiaan Vroegindeweij, Angelo Mencarelli, and Eldert van Henten with the following affiliation for Angelo Mencarelli: Stichting Wageningen Research, Wageningen University & Research, Wageningen, The Netherlands.

A bottleneck of state-of-the-art machine learning methods, e.g. deep learning, for plant part image segmentation in agricultural robotics is the requirement of large manually annotated datasets. As a solution, large synthetic datasets including ground truth can be rendered that realistically reflect the empirical situation. However, a dissimilarity gap can remain between synthetic and empirical data by incomplete manual modelling. This paper contributes to closing this gap by optimising the realism of synthetic agricultural images using unsupervised cycle generative adversarial networks, enabling unpaired image-to-image translation from the synthetic to empirical domain and vice versa. For this purpose, the Capsicum annuum (sweet- or bell pepper) dataset was used, containing 10,500 synthetic and 50 empirical annotated images. Additionally, 225 unlabelled empirical images were used. We hypothesised that the similarity of the synthetic images with the empirical images increases qualitatively and quantitively when translated to the empirical domain and investigated the effect of the translation on the factors color, local texture and morphology. Results showed an increased mean class color distribution correlation with the empirical dataset from 0.62 prior and 0.90 post translation of the synthetic dataset. Qualitatively, synthetic images translate very well in local features such as color,illumination scattering and texture. However, global features like plant morphology appeared not to be translatable.

A problem in loose housing systems for layers is laying eggs on the floor, which need manual collection. To automate this, it is desired to know the location of floor eggs for planning a collection path. as this information is not available, we constructed a spatial model to indicate the probability on floor eggs, based on housing properties. This model is mainly determined by parameters relating probability to position in the house. Validation against floor egg locations from poultry practice indicated that underlying model assumptions match with practice, making the model a suitable start for further work in this field.