In the realm of agricultural research, the creation of open-field plot grids is essential for structuring experiments, such as testing different crop varieties or growing conditions systematically, particularly in field trials for vegetables.
This conceptual map or grid is an important component of the Unmanned Aerial Vehicle (UAV) image processing workflow, as it is used as a digital overlay to analyze and manage data collected from those plots.
Traditionally, the creation of open-field plot grids for UAV image processing in vegetable trials has heavily relied on manual GPS measurements. However, this method is beset with challenges, notably being time-consuming, labor-intensive, and susceptible to errors. These difficulties are particularly pronounced in scenarios where high-precision transplanters are not readily available.
In these cases, seedlings are often planted manually, which can lead to significant variability in plant spacing and depth. When planting patterns are inconsistent, leading to unevenly spaced or misaligned crops, it becomes more difficult to accurately identify and delineate individual plots in the images captured by UAVs.
Without precise plot GPS measurements, the UAV image processing workflow cannot generate accurate crop traits or correctly assign those traits to individual plots.
Thus, there exists a pressing need for a more efficient, accurate, and reliable solution to facilitate the creation of open-field plot grids in vegetable trials.
We are looking for an innovative and efficient automated workflow or technology that can rapidly generate a geospatially accurate grid for the entire field with minimal manual labor. The proposed solution should eliminate the need for manual GPS measurements and ensure the creation of a precise and complete as-planted grid, integrating seamlessly with the existing UAV image processing workflow.
While the options below represent potential solutions, we are open to other technologies or systems that can automatically recognize and delineate individual plots directly from UAV images or through integration with UAV data, creating a precise, geospatially accurate grid.
Bayer’s vision of #HealthForAll, #HungerForNone drives our need to strengthen innovation capabilities in all areas of agriculture. We know we can’t accomplish this alone, so we're always interested to hear about novel, early-stage scientific innovations that can contribute to feeding the world without starving the planet. You have our commitment to take a look, match with our R&D priorities and provide you timely feedback.
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