  In viticulture, there are several applications where bud detection in
vineyard images is a necessary task, susceptible of being automated through the
use of computer vision methods. A common and effective family of visual
detection algorithms are the scanning-window type, that slide a (usually) fixed
size window along the original image, classifying each resulting windowed-patch
as containing or not containing the target object. The simplicity of these
algorithms finds its most challenging aspect in the classification stage.
Interested in grapevine buds detection in natural field conditions, this paper
presents a classification method for images of grapevine buds ranging 100 to
1600 pixels in diameter, captured in outdoor, under natural field conditions,
in winter (i.e., no grape bunches, very few leaves, and dormant buds), without
artificial background, and with minimum equipment requirements. The proposed
method uses well-known computer vision technologies: Scale-Invariant Feature
Transform for calculating low-level features, Bag of Features for building an
image descriptor, and Support Vector Machines for training a classifier. When
evaluated over images containing buds of at least 100 pixels in diameter, the
approach achieves a recall higher than 0.9 and a precision of 0.86 over all
windowed-patches covering the whole bud and down to 60% of it, and scaled up to
window patches containing a proportion of 20%-80% of bud versus background
pixels. This robustness on the position and size of the window demonstrates its
viability for use as the classification stage in a scanning-window detection
algorithms.
