  Trunk diameter is a variable of agricultural interest, used mainly in the
prediction of fruit trees production. It is correlated with leaf area and
biomass of trees, and consequently gives a good estimate of the potential
production of the plants. This work presents a low cost, high precision method
for the measurement of trunk diameter of grapevines based on Computer Vision
techniques. Several methods based on Computer Vision and other techniques are
introduced in the literature. These methods present different advantages for
crop management: they are amenable to be operated by unknowledgeable personnel,
with lower operational costs; they result in lower stress levels to
knowledgeable personnel, avoiding the deterioration of the measurement quality
over time; and they make the measurement process amenable to be embedded in
larger autonomous systems, allowing more measurements to be taken with
equivalent costs. To date, all existing autonomous methods are either of low
precision, or have a prohibitive cost for massive agricultural adoption,
leaving the manual Vernier caliper or tape measure as the only choice in most
situations. In this work we present a semi-autonomous measurement method that
is susceptible to be fully automated, cost effective for mass adoption, and its
precision is competitive (with slight improvements) over the caliper manual
method.
