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The ability to accurately represent and localise relevant objects is essential for robots to carry out tasks effectively. Traditional approaches, where robots simply capture an image, process that image to take an action, and then forget the information, have proven to struggle in the presence of occlusions. Methods using multi-view perception, which have the potential to address some of these problems, require a world model that guides the collection, integration and extraction of information from multiple viewpoints. Furthermore, constructing a generic representation that can be applied in various environments and tasks is a difficult challenge. In this paper, a novel approach for building generic representations in occluded agro-food environments using multi-view perception and 3D multi-object tracking is introduced. The method is based on a detection algorithm that generates partial point clouds for each detected object, followed by a 3D multi-object tracking algorithm that updates the representation over time. The accuracy of the representation was evaluated in a real-world environment, where successful representation and localisation of tomatoes in tomato plants were achieved, despite high levels of occlusion, with the total count of tomatoes estimated with a maximum error of 5.08% and the tomatoes tracked with an accuracy up to 71.47%. Novel tracking metrics were introduced, demonstrating that valuable insight into the errors in localising and representing the fruits can be provided by their use. This approach presents a novel solution for building representations in occluded agro-food environments, demonstrating potential to enable robots to perform tasks effectively in these challenging environments. • A novel method to build world models for robots based on 3D object tracking. • A real world evaluation of our method in highly occluded scenarios. • Detection and association performance decreases with occlusions. • 3D object tracking yields an accuracy up to 71%. • Tracking metrics give more insight than the total count of objects. [ABSTRACT FROM AUTHOR]

Combination of production of algae and tomato increases efficient use of available resources of greenhouse enterprises, such as controlled environment, water and nutrients, carbon dioxide, greenhouse space and infrastructure and knowledge. No information is available, however, about the potential productivity and related costs of a combined tomato and algae production in Dutch greenhouses. The objective was to determine the algae productivity in tubular photobioreactors (PBRs) and the economic feasibility of combined production of tomato and algae in Dutch greenhouses. A model was developed to predict greenhouse climate from outside climate, to predict tomato and algae biomass production and to analyse scenarios of different locations and dimensions of tubular PBR in the greenhouse with regard to algae productivity and cost price of algae production. The results show that algal productivity is low if PBRs are installed under a tomato crop due to limited light levels. Areal algal productivity was calculated to be 5–6.5 kg DM m−2 if PBRs are installed in a separate greenhouse compartment next to tomato. In this case the minimum cost prices of algae production was calculated to be €11 kg−1 DM algae, which give perspectives for the future. The proposed model is important because it gives insight into the feasibility of algae and tomato production in Dutch greenhouses. This novel model approach and the scenario results provide better knowledge about the potential productivity and related costs and returns of algae production in greenhouses. [ABSTRACT FROM AUTHOR]

Measurement of volatile organic compounds (VOCs) emitted by plants allows us to monitor plant health status without touching the plant. To bring this technique a step further towards a practical plant diagnosis technique for greenhouse crop production, we have defined a numerical index named “Emission index” to evaluate VOC emissions from tomato plants isolated from ambient greenhouse air using an open-bottom chamber. The emission index of a VOC is a ratio of the concentration of the VOC in the air inside the chamber to that in the ambient greenhouse air and the index should be larger than one if the VOC is emitted by the plants in the chamber. Measurement of the emission indices of representative tomato VOCs proved that non-stressed tomato plants in a greenhouse emit n-hexanal, 2-carene, β-phellandrene, α-copaene, β-caryophyllene and (3E,7E)-4,8,12-trimethyl-1,3,7,11-tridecatetraene (TMTT), do not emit (Z)-3-hexenol, and might absorb and/or adsorb n-hexanol. Another experiment proved that a routine crop maintenance operation, i.e. removal of old leaves from the lower part of plants and removal of side shoots, enhances the emissions of n-hexanal, (E)-2-hexenal, (Z)-3-hexenol, but does not enhance the emissions of α-copaene, methyl salicylate (MeSA) and TMTT. The results suggest that the measurement of emission index with the open-bottom chamber is a useful technique for monitoring VOC emissions by plants in a greenhouse under practical conditions. [ABSTRACT FROM AUTHOR]

Crop modelling is an essential part of biosystems engineering; selecting or developing a crop model for a specific application, having its requirements and desires, is difficult if not impossible without the required domain knowledge. This paper presents a fundamentally different model selection approach based on biological functionalities. This is enabled by a common structure, which allows for a combining of components, yielding new models. This increased design space allows the development of models which are better suited to the application than the original models. The use of a common structure, and its potential, are demonstrated by a use-case involving the selection of a tomato crop model for a model-based control application, but the rationales and methodologies can apply to other crops and applications as well. In this paper, 27 valid model combinations have been created from 4 models. In the use-case presented, the models are compared to data originating from a real system. The predictive performance of a model is quantified by the Root-Mean-Squared-Error (RSME) between the predictions and data. One trade-off is model accuracy versus computational speed. With the model set used, a 13% decrease in RSME was obtained by allowing a 7.5% increase in model computation time compared to one of the original models. • A fundamentally different model selection approach using biological functionalities. • 27 valid model combinations have been created from 4 models. • A model combination has been found which predicts better than the original models. [ABSTRACT FROM AUTHOR]