Abstract
Unmanned aerial vehicle (UAV) based active canopy sensors can serve as a promising sensing solution for the estimation of crop nitrogen (N) status with great applicability and flexibility. This study was endeavored to determine the feasibility of UAV-based active sensing to monitor the leaf N status of rice (Oryza sativa L.) and to examine the transferability of handheld-based predictive models to UAV-based active sensing. In this 3-year multi-locational study, varied N-rates (0–405 kg N ha−1) field experiments were conducted using five rice varieties. Plant samples and sensing data were collected at critical growth stages for growth analysis and monitoring. The portable active canopy sensor RapidSCAN CS-45 with red, red edge, and near infrared wavebands was used in handheld mode and aerial mode on a gimbal under a multi-rotor UAV. The results showed the great potential of UAV-based active sensing for monitoring rice leaf N status. The vegetation index-based regression models were built and evaluated based on Akaike information criterion and independent validation to predict rice leaf dry matter, leaf area index, and leaf N accumulation. Vegetation indices composed of near-infrared and red edge bands (NDRE or RERVI) acquired at a 1.5 m aviation height had a good performance for the practical application. Future studies are needed on the proper operation mode and means for precision N management with this system.
Highlights
Nitrogen (N) plays a vital role in improving crop growth and productivity (Novoa and Loomis, 1981; Ata-Ul-Karim et al, 2016)
624 samples which have a wide range of leaf dry matter (LDM) (51.93 kg ha−1 to 5313.40 kg ha−1), leaf area index (LAI) (0.13 to 10.34), and Leaf nitrogen accumulation (LNA) (0.96 kg ha−1 to 192.61 kg ha−1) were involved in the calibration experiments
This study has shown that the Unmanned aerial vehicle (UAV) mounted active canopy sensor is feasible for monitoring rice N-status, yet some points, including sensing distance, canopy perturbance from air movement, and the slightly unstable flight condition caused by the aerodynamic ground effect of a low-altitude flight, still need to be addressed for the practical use of this sensing system
Summary
Nitrogen (N) plays a vital role in improving crop growth and productivity (Novoa and Loomis, 1981; Ata-Ul-Karim et al, 2016). Over-application of N fertilizers is the alarming issue that has caused low N use efficiency, leading to N deposition and water eutrophication (Conant et al, 2013; Liu et al, 2013; Huang et al, 2017). It is imperative to develop highly efficient, reliable and practical methods for monitoring crop N status to meet the demand for precision N management (Miao et al, 2011). Several traditional methods, such as the leaf color chart (Alam et al, 2005) or destructive chemical analysis (Asner and Martin, 2008) are limited by low efficiency, UAV-Based Active Sensing small-scale applicability, and professional experience requirements for accurate diagnosis. With the advances of optical sensors and remote sensing technology, crop N-status monitoring, and management based on the spectrum has been widely used in different crops (Saberioon et al, 2014; Padilla et al, 2018)
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