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

Computer Vision Approach for Low Cost, High Precision Measurement of Grapevine Trunk Diameter in Outdoor Conditions