![]() ![]() These residues are composed of leaves and stems. Harvesting food crops results in the yearly production of around 40 tonnes (for sugar beet) to 50 tonnes (for tomatoes) of crop residues per hectare. These might include producers of dairy and meat substitutes,” says Bruins. ![]() “That could mean working with greenhouse horticulture businesses, or businesses that use plant-based proteins as inputs. The researchers hope to work with the private sector to further develop the technology to apply it on an industrial scale. “Our study proves that you can achieve substantial gains in sustainability by making better use of what you already have. “Our method filters out the components that are smaller than the protein we want to extract, and this includes many toxins,” says project leader Marieke Bruins, a senior scientist in protein technology at Wageningen University & Research. The leaves of potato and cassava plants, for example, also contain toxins, and like tomato leaves they therefore are unsuitable for direct consumption. The same method could also be suitable for extracting Rubisco from the leaves of other food crops. The result was a high-value protein powder which was free of toxins. The researchers investigated whether they could use this method to also remove the toxin hydroxytomatine from tomato leaves. Results will be discussed with stakeholders.The pilot study was based on a method of extracting Rubisco from sugar beet leaves. Testing will be conducted at the lab or greenhouse scale, either at WUR or at producer organisations. The VTC is evaluated from an integrated and practical perspective and based on technical and economic performance. Furthermore, the digital twin can be used to virtually explore leaf pruning strategies, to test different greenhouse cover types, and to select superior crop traits. Research questions concern effects of model granularity on climate control advice, and the effect of daily crop status update on control performance in terms of light use efficiency. The output of the model will be used for automatic control of greenhouse climate settings. ![]() We will use and develop deep-learning methods to obtain morphological, reflectance, and physiological traits. The focus is therefore on estimating plant traits from sensor data. In real time, data on plant growth and growing conditions will be captured using the NPEC greenhouse facilities (Data from several sensors in the NPEC facilities, such as the multi-spectral 3D laser scanner, chlorophyll fluorescence camera, thermal camera and climate sensors, will be processed to estimate plant traits and climate conditions. The VTC will be continuously updated with data from the real twin a tomato crop growing in the greenhouse. The environmental variables driving plant growth and development will be simulated by a greenhouse module based on the Kaspro model. Crop behaviour is thus the result of individual plants using shared resources. The crucial property of FSP models is that growth and development of the plants feedback on the resources driving growth, in terms of increased shading and depletion of nutrients and water. The Virtual Tomato Crops (VTC) model is based on the concepts of functional-structural plant modelling, which simulates individual plants and their functioning as well as their 3D architectural development. ![]()
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