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Accuracy of NDVI-derived corn yield predictions is impacted by time of sensing

机译:NDVI衍生的玉米产量预测的准确性受到传感时的影响

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Normalized difference vegetation index (NDVI) data derived from cameras mounted on unmanned aerial vehicles (UAVs) or from tractor-mounted sensors have the potential to aid in quick and accurate decision making for fertility management of corn (Zea mays L.). However, timing of sensing can impact the accuracy of yield predictions and hence nutrient need assessment. In this study the accuracy of yield predictions derived from NDVI data for corn silage and grain was evaluated using (a) an active sensor (GreenSeeker; handheld unit), and (b) a passive sensor (UAV-mounted camera). Two types of replicated trials were conducted in central New York: (a) N rate studies with N applied at V6 at rates ranging from 0 kg N ha(-1) to 336 kg N ha(-1) (2016, 2017, and 2018); and (b) timing of N application studies, with N applied at V4, V6, V8 or V10 (2017 and 2018). For each of the trials conducted in 2017 and 2018, the crop was sensed and NDVI was determined weekly from V4 through V12. An additional flight was done with the UAV-mounted camera at R2. In addition, the 2016 N rate study and timing of sidedress study in 2018 were sensed at V10 (single day) from 7 am through 5 pm on 2-hour intervals. Results showed that crop growth stage and timing of N application affected the accuracy of yield predictions. Scans within 2 weeks of sidedressing did not result in accurate yield predictions, independent of sensor type. Best yield estimates were obtained with a UAV-mounted camera late in the season (R2 growth stage). For in-season N management, a low-rate ( < 30 kg N ha(-1)) starter N application is recommended. Sensing must be delayed until just after initial N deficiencies become visible. Sidedressing at V10 still resulted in a yield increase beyond yields with starter N only, but if such late applications are done, accurate estimates of yield based on NDVI can only be obtained with sensing a second time after V12. Due to the reaction of corn plants to water stress, timing of sensing within a day showed the best yield predictions between 9 and 11 am and at 5 pm, independent of sensor used. For most accurate yield predictions, we recommend scanning between V12 and R2, avoiding early morning (dew) and mid-day hours (1-3 pm) if water stress in the crop is evident.
机译:归一化差异植被指数(NDVI)来自安装在无人机(无人机)或拖拉机安装的传感器上的摄像机衍生的数据有可能有助于提供玉米生育管理的快速和准确的决策,用于玉米(Zea Mays L.)。然而,感测的时间可以影响产量预测的准确性,因此营养需求评估。在这项研究中,使用(a)有源传感器(Greenseeker;手持单元)评估从用于玉米青贮和谷物的NDVI数据的产量预测的准确性,并使用无源传感器(UAV安装的相机)。在纽约市中心进行两种复制试验:(a)N速率研究在V6的速率下施加到0 kg n ha(-1)至336 kg n ha(-1)(2016年,2017年,和2018); (b)N施用研究的时间,N施用在V4,V6,V8或V10(2017和2018)。对于2017年和2018年进行的每项试验,感测作物,并从V4到V12每周确定NDVI。额外的飞行是在R2处用无人机安装的相机完成的。此外,2018年2018年的2016年比率研究和时间研究于2018年的Sidentress研究,在v10(单一日期)从下午7点到下午5点到2小时间隔。结果表明,N应用的作物生长阶段和时序影响了产量预测的准确性。扫描在2周内偏见的偏见并未导致准确的产量预测,与传感器类型无关。在季节晚期的覆盖式摄像头(R2生长阶段),获得了最佳收益率估计。对于季节N管理,建议使用低速率(<30千克(-1))启动N应用。在初始n缺陷变得可见之前,必须延迟感测。在V10的Sidentrings仍然导致屈服增加,超出起动器N,但如果完成了这些晚期应用,则只能在V12之后第二次感测基于NDVI的基于NDVI的准确估计。由于玉米植物与水胁迫的反应,每天内测量的时间显示在9到11点和5μm之间的最佳产量预测,与所使用的传感器无关。对于大多数准确的产量预测,我们建议在V12和R2之间扫描,避免清晨(露水)和中期时间(1-3 PM)如果作物中的水分显而易见。

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