{
  "id": "1406.4845",
  "version": "v2",
  "published": "2014-05-16T18:44:58.000Z",
  "updated": "2016-05-09T21:10:16.000Z",
  "title": "Computer Vision Approach for Low Cost, High Precision Measurement of Grapevine Trunk Diameter in Outdoor Conditions",
  "authors": [
    "Diego Sebastián Pérez",
    "Facundo Bromberg",
    "Francisco Gonzalez Antivilo"
  ],
  "comment": "10 pages, 5 figures",
  "categories": [
    "cs.CV"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2014-05-16T18:44:58.000Z",
      "title": "Low Cost, High Precision, Autonomous Measurement of Trunk Diameter based on Computer Vision",
      "abstract": "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 potential production of the plants. This work presents a low cost, high precision method for autonomous measurement of trunk diameter of fruit trees based on Computer Vision. Autonomous methods based on Computer Vision or other techniques are introduced in the literature for they present important simplifications in the measurement process, requiring little to none human decision making. This presents different advantages for crop management: the method is amenable to be operated by unknowledgeable personnel, with lower operational costs; it results in lower stress levels to knowledgeable personnel, avoiding the deterioration of the measurement quality over time; or it makes the measurement process amenable to be embedded in larger autonomous systems, allowing more measurement to be taken with equivalent costs. In a more personal aspect, the present work is also a successful proof-of-concept for our laboratories and regional research institutions in favor of autonomous measurements based on Computer Vision, opening the door to further investigations in other important agronomic variables measurable by Computer Vision. 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 an autonomous solution that is costly effective for mass adoption, and its precision is competitive (with slight improvements) over the caliper method.",
      "comment": "16 pages, 6 figures",
      "journal": null,
      "doi": null
    },
    {
      "version": "v2",
      "updated": "2016-05-09T21:10:16.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "computer vision",
      "trunk diameter",
      "autonomous measurement",
      "low cost",
      "high precision"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 10,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2014arXiv1406.4845S"
    }
  }
}