Node‐by‐node boll mapping has been used to determine the effects of several environmental and management strategies, including irrigation rate and cultivar, on cotton (Gossypium hirsutum L.) boll distribution. The advent of consumer 3D digital imaging may make rapid node‐by‐node mapping of greater acreage possible in the future. Effects of irrigation rate and cultivar on boll distribution were determined from line plots of node‐specific boll distribution and boll accumulation estimate data and from vertical box and whisker plots of boll fraction using data collected by a 3D sensor system to rapidly detect open bolls before harvest over three seasons. Differences were observed between the dryland (2018 only), low irrigation, and high irrigation rates in terms of boll fraction and boll accumulation for each cultivar. The low irrigation tended to produce bolls more toward the bottom of the plant, while the high irrigation produced bolls towards the top of the plant. Yield correlation between sensor obtained and manually counted measurements was strong, with r² values as high as 0.87. Results obtained indicated that differences among irrigation rate and cultivar were identifiable using the sensor system during the 3 yr research study. Similar systems may allow rapid, broad‐scale identification of boll distribution and yield in breeding and physiology research in the future, leading to improved identification of elite cultivars and improved management practices in cotton.

中文翻译：

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利用三维点云数据估算棉铃分布和单产

逐节点棉铃制图已被用来确定几种环境和管理策略，包括灌溉速率和品种对棉花的影响（陆地棉） L.）铃分布。消费者3D数字成像技术的出现可能在将来使更大面积的快速逐节点映射成为可能。灌溉速率和品种对铃铃分布的影响是通过节点特定铃铃分布和铃铃积累估计数据的线图，以及通过使用3D传感器系统收集的数据来快速检测收获前的开铃的铃虫分数的垂直箱形图和晶须图来确定的超过三个季节。干旱地区（仅2018年），低灌溉率和高灌溉率之间在每个品种的铃铃分数和铃铃积累方面存在差异。较低的灌溉量往往会向植株的底部产生更多的棉铃，而较高的灌溉量则倾向于向植株的顶部移动的棉铃。r ²值高达0.87。获得的结果表明，在3年的研究期间，使用传感器系统可以识别灌溉速率和品种之间的差异。类似的系统可能会在将来的育种和生理研究中快速，广泛地识别铃铃的分布和产量，从而导致对优良品种的鉴定和棉花管理实践的改善。