12 Like Us on Facebook: BetterFarmingON Better Farming | August 2024 Researchers at the University of Bonn have developed software that can simulate the growth of field crops. To do this, they fed thousands of photos from field experiments into a learning algorithm. This enabled the algorithm to learn how to visualize the future development of cultivated plants based on a single initial image. Using the images created during this process, parameters such as leaf area or yield can be estimated accurately. The results have been published in the journal Plant Methods. Which plants should I combine in what ratio to achieve the greatest possible yield? And how will my crop develop if I use manure instead of artificial fertilizers? In the future, farmers should increasingly be able to count on computer support when answering such questions. Researchers at the University of Bonn have now taken a crucial step forward on the path towards this goal: “We have developed software that uses drone photos to visualize the future development of the plants shown,” explains Lukas Drees from the Institute of Geodesy and Geoinformation at the university. The early career researcher is an employee in the Cluster of Excellence PhenoRob. The large-scale project based at the University of Bonn intends to drive forward the intelligent digitalization of agriculture to help farming become more environmentally friendly without causing harvest yields to suffer. A virtual decision-making aid The computer program now presented by Drees and his colleagues in Plant Methods is an important building block. It should eventually make it possible to simulate certain decisions virtually to assess how the use of pesticides or fertilizers will affect crop yield. For this to work, the program must be fed with drone photos from field experiments. “We took thousands of images over one growth period,” explains Drees. “In this way, for example, we documented the development of cauliflower crops under certain conditions.” The researchers then trained a learning algorithm using these images. Afterwards, based on a single aerial image of an early stage of growth, this algorithm was able to generate images showing the future development of the crop in a new, artificially created image. The whole process is very accurate as long as the crop conditions are similar to those present when the training photos were taken. Consequently, the software does not take into account the effect of a sudden cold snap or steady rain lasting several days. However, it should learn in the future how growth is affected by influences such as these, as well as an increased use of fertilizers, for example. This should enable it to predict the outcome of certain interventions by the farmer. “In addition, we used a second AI software that can estimate various parameters from plant photos, such as crop yield,” says Drees. “This also works with the generated images. It is thus possible to estimate Research MODELLING CROP GROWTH WITH AI The software uses drone photos to visualize future plant development. Based on a release from University of Bonn Researchers have been supplying the program with drone photos from field experiments. Jana - stock.adobe.com photo
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