室外条件下葡萄树干直径低成本高精度测量的计算机视觉方法
Computer Vision Approach for Low Cost, High Precision Measurement of Grapevine Trunk Diameter in Outdoor Conditions
摘要：
树干直径是一个农业变量，主要用于果树产量的预测。它与树木的叶面积和生物量相关，因此可以很好地估计植物的潜在生产力。提出了一种基于计算机视觉技术的低成本、高精度的葡萄树干直径测量方法。文献中介绍了几种基于计算机视觉和其他技术的方法。这些方法为作物管理提供了不同的优势：它们适合由不懂知识的人员操作，具有较低的运营成本;它们对知识渊博的人员产生较低的压力水平，避免测量质量随时间推移而恶化;并且它们使测量过程适合嵌入较大的自主系统中，允许以相等的成本进行更多的测量。到目前为止，所有现有的自主测量方法要么精度低，要么成本过高，无法大规模农业应用，因此手动游标卡尺或卷尺在大多数情况下是唯一的选择。在这项工作中，我们提出了一种半自主的测量方法，易于被完全自动化，成本效益的大规模采用，其精度是有竞争力的（略有改善）卡尺手动方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
