Three-dimensional high throughput plant phenotyping techniques provide an opportunity to measure plant organ-level traits which can be highly useful to plant breeders. The number and locations of cotton bolls, which are the fruit of cotton plants and an important component of fiber yield, are arguably among the most important phenotypic traits but are complex to quantify manually. Hence, there is a need for effective and efficient cotton boll phenotyping solutions to support breeding research and monitor the crop yield leading to better production management systems. We developed a novel methodology for 3D cotton boll mapping within a plot in situ. Point clouds were reconstructed from multi-view images using the structure from motion algorithm. The method used a region-based classification algorithm that successfully accounted for noise due to sunlight. The developed density-based clustering method could estimate boll counts for this situation, in which bolls were in direct contact with other bolls. By applying the method to point clouds from 30 plots of cotton plants, boll counts, boll volume and position data were derived. The average accuracy of boll counting was up to 90% and the R² values between fiber yield and boll number, as well as fiber yield and boll volume were 0.87 and 0.66, respectively. The 3D boll spatial distribution could also be analyzed using this method. This method, which was low-cost and provided improved site-specific data on cotton bolls, can also be applied to other plant/fruit mapping analysis after some modification.

三维高通量植物表型分析技术为测量植物器官水平的性状提供了机会，这对植物育种者非常有用。棉铃是棉株的果实，是纤维产量的重要组成部分，其数量和位置可以说是最重要的表型性状，但要手动定量比较复杂。因此，需要有效和高效的棉铃表型鉴定解决方案，以支持育种研究和监测农作物产量，从而建立更好的生产管理系统。我们开发了一种新颖的方法，用于在原地内进行3D棉铃测绘。使用运动算法的结构从多视图图像重建点云。该方法使用了基于区域的分类算法，该算法成功地解决了由于阳光引起的噪声。发达的基于密度的聚类方法可以估计这种情况下的棉铃数，此时棉铃与其他棉铃直接接触。通过将该方法应用于来自30个棉田的点云，推导了棉铃数，棉铃体积和位置数据。弹药计数的平均准确度高达90％，R ²纤维产量和铃数之间的值以及纤维产量和铃量分别为0.87和0.66。也可以使用此方法分析3D铃的空间分布。这种方法成本低廉，并且提供了有关棉铃的特定地点的改进数据，经过修改后，该方法也可以应用于其他植物/水果作图分析。