International Center for Agro-Informatics and Sustainable Development, College of Resources and Environmental Sciences, China Agricultural University, Beijing, 100193, China.
Department of Geography, Minnesota State University, Mankato, MN, 56001, USA.
Sci Rep. 2017 Oct 26;7(1):14073. doi: 10.1038/s41598-017-14597-1.
RapidSCAN is a new portable active crop canopy sensor with three wavebands in red, red-edge, and near infrared spectral regions. The objective of this study was to determine the potential and practical approaches of using this sensor for non-destructive diagnosis of rice nitrogen (N) status. Sixteen plot experiments and ten on-farm experiments were conducted from 2014 to 2016 in Jiansanjiang Experiment Station of the China Agricultural University and Qixing Farm in Northeast China. Two mechanistic and three semi-empirical approaches using the sensor's default vegetation indices, normalized difference vegetation index and normalized difference red edge, were evaluated in comparison with the top performing vegetation indices selected from 51 tested indices. The results indicated that the most practical and stable method of using the RapidSCAN sensor for rice N status diagnosis is to calculate N sufficiency index with the default vegetation indices and then to estimate N nutrition index non-destructively (R = 0.50-0.59). This semi-empirical approach achieved a diagnosis accuracy rate of 59-76%. The findings of this study will facilitate the application of the RapidSCAN active sensor for rice N status diagnosis across growth stages, cultivars and site-years, and thus contributing to precision N management for sustainable intensification of agriculture.
RapidSCAN 是一种新型的便携式主动作物冠层传感器,具有红、红边和近红外三个波段。本研究的目的是确定该传感器用于无损诊断水稻氮(N)状况的潜力和实用方法。2014 年至 2016 年,在中国农业大学建三江实验站和中国东北七星农场进行了 16 个田间试验和 10 个田间试验。评估了使用传感器默认植被指数归一化差异植被指数和归一化差异红边的两种机械和三种半经验方法,与从 51 个测试指数中选择的表现最佳的植被指数进行比较。结果表明,使用 RapidSCAN 传感器进行水稻 N 状况诊断最实用和稳定的方法是使用默认植被指数计算 N 充足指数,然后无损估算 N 营养指数(R=0.50-0.59)。这种半经验方法的诊断准确率为 59-76%。本研究的结果将有助于 RapidSCAN 主动传感器在整个生长阶段、品种和地点-年份应用于水稻 N 状况诊断,从而有助于实现农业可持续集约化的精确 N 管理。