Dynamic segmentation to estimate vine vigor from ground images

V. Sáiz-Rubio, F. Rovira-Más
2012 Spanish Journal of Agricultural Research  
The geographic information required to implement precision viticulture applications in real fields has led to the extensive use of remote sensing and airborne imagery. While advantageous because they cover large areas and provide diverse radiometric data, they are unreachable to most of medium-size Spanish growers who cannot afford such image sourcing. This research develops a new methodology to generate globally-referenced vigor maps in vineyards from ground images taken with a camera mounted
more » ... h a camera mounted on a conventional tractor. This monocular camera was able to sense in the visible, NIR, and UV spectra, selectively isolated with bandpass filters. The versatility of the system was further enhanced by implementing two sampling levels: intensive coverage of 1 m 2 and super-intensive for 0.1 m 2 . The core of the procedure resides in the algorithm for automatically segmenting the filtered images in such a way that relative differences in canopy vigor were objectively quantified. The calculation of the dynamic threshold involved the mathematical concepts of gradient and curvature. Field results showed that relative differences in vine vigor can be detected from NIR-filtered images and intensive sampling. Furthermore, individual images were successfully merged into a global vigor map that can be directly employed by end-users. Super-intensive sampling and UV perception were not appropriate for building vigor maps, but could be of interest for other agronomical purposes as the early detection of diseases. Field tests proved the feasibility of building global vigor maps from ground-based imagery, and showed the potential of this technique as a predictive instrument for modest-size producers. Abbreviations used: FOV (field of view); GIS (geographic information system); NDVI (normalized difference vegetation index); NIR (near-infrared); PA (precision agriculture); UV (ultraviolet); VLAI (vertical leaf area index).
doi:10.5424/sjar/2012103-508-11 fatcat:xjyxhzgmzjfrdjyywxyj6j6dam