Honorable Mention Winner
As the world population increases, so do the demands for more efficient (less energy-consuming) methods of food cultivation. The vision of “precision agriculture” strives for greater farmland efficiency through advances in technology (sensors, robots), promising capabilities beyond what is possible from only manual labor. The University of North Florida’s “Garduino” project, providing a “hands-on” garden bed within which customized automated solutions can be piloted, aims to prepare engineering and computing undergraduates for this precision agriculture vision. A particularly valuable data source in this vision is near-field crop imagery, as may be acquired via self-navigating ground robots with cameras, for example. We report on experiences with using the image processing toolbox in MATLAB to measure green pixel density to monitor growth and health in plants, assuming increasing green density is an indicator of plant growth. Our approach depends upon a two-step image enhancement algorithm, first transforming the RGB color input image into its HSV parameterization and then employing a threshold-based intensity transformation. Error is quantified by comparing the algorithm’s results to the manual determination of the plant’s growth and health. Preliminary results support the usefulness of our approach. Future research will generalize the algorithm and broaden the scope of its agricultural applications
