Using the images created during this process, parameters such as leaf area or yield can be estimated accurately.
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 the doctoral researcher."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.
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. These -- metaphorically speaking -- 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," stresses Drees.
Source: Education Headlines (educationheadlines.net)
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