  In Viticulture, visual inspection of the plant is a necessary task for
measuring relevant variables. In many cases, these visual inspections are
susceptible to automation through computer vision methods. Bud detection is one
such visual task, central for the measurement of important variables such as:
measurement of bud sunlight exposure, autonomous pruning, bud counting,
type-of-bud classification, bud geometric characterization, internode length,
bud area, and bud development stage, among others. This paper presents a
computer method for grapevine bud detection based on a Fully Convolutional
Networks MobileNet architecture (FCN-MN). To validate its performance, this
architecture was compared in the detection task with a strong method for bud
detection, Scanning Windows (SW) based on a patch classifier, showing
improvements over three aspects of detection: segmentation, correspondence
identification and localization. The best version of FCN-MN showed a detection
F1-measure of $88.6\%$ (for true positives defined as detected components whose
intersection-over-union with the true bud is above $0.5$), and false positives
that are small and near the true bud. Splits -- false positives overlapping the
true bud -- showed a mean segmentation precision of $89.3\% (21.7)$, while
false alarms -- false positives not overlapping the true bud -- showed a mean
pixel area of only $8\%$ the area of a true bud, and a distance (between mass
centers) of $1.1$ true bud diameters. The paper concludes by discussing how
these results for FCN-MN would produce sufficiently accurate measurements of
bud variables such as bud number, bud area, and internode length, suggesting a
good performance in a practical setup.
