13 Like Us on Facebook: BetterFarmingON Better Farming | August 2024 quite precisely the subsequent size of the cauliflower heads at a very early stage in the growth period.” Focus on polycultures One area the researchers are focusing on is the use of polycultures. This refers to the sowing of different species in one field, such as beans and wheat. As plants have different requirements, they compete less with each other in a polyculture of this kind compared to a monoculture, where just one species is grown. This boosts yield. In addition, some species – beans are a good example of this – can bind nitrogen from the air and use it as a natural fertilizer. The other species, in this case wheat, also benefits from this. “Polycultures are also less susceptible to pests and other environmental influences,” explains Drees. “However, how well the whole thing works very much depends on the combined species and their mixing ratio.” When results from many different mixing experiments are fed into learning algorithms, it is possible to derive recommendations as to which plants are particularly compatible and in what ratio. Plant growth simulations on the basis of learning algorithms are a relatively new development. Process-based models have mostly been used for this purpose up to now. Metaphorically speaking, these models have a fundamental understanding of what nutrients and environmental conditions certain plants need during their growth in order to thrive. “Our software, however, makes its statements solely based on the experience they have collected using the training images,” says Drees. Both approaches complement each other. If they were to be combined in an appropriate manner, it could significantly improve the quality of the forecasts. “This is also a point that we are investigating in our study,” says the researcher. “How can we use process- and image-based methods so they benefit from each other in the best possible way?” The University of Bonn and Forschungszentrum Jülich took part in the study. The work was funded by the German Research Foundation (DFG) as part of the German Excellence Strategy. BF The work is published as Lukas Drees, Dereje T. Demie, Madhuri R. Paul, Johannes Leonhardt, Sabine J. Seidel, Thomas F. Döring, Ribana Roscher: Data-driven Crop Growth Simulation on Time-varying Generated Images using Multi-conditional Generative Adversarial Networks; Plant Methods; https://doi.org/10.1186/s13007-02401205-3, URL: https://plantmethods. biomedcentral.com/articles/10.1186/ s13007-024-01205-3 Research FILTRATION YOU CAN TRUST. WIX® heavy-duty filters are built to withstand the rigorous demands of the ag industry. By extending service intervals and reducing downtime, the right filters can increase your production and yield. WIX filters are tested and trusted—even in the harshest conditions. WIX-003083-04_2024 Trade Media HD_Agriculture_V4.indd 1 4/10/24 3:33 PM
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