Abstract
Unmanned aerial system (UAS)-based remote sensing is one promising technique for precision crop management, but few studies have reported the applications of such systems on nitrogen (N) estimation with multiple sensors in rice (Oryza sativa L.). This study aims to evaluate three sensors (RGB, color-infrared (CIR) and multispectral (MS) cameras) onboard UAS for the estimation of N status at individual stages and their combination with the field data collected from a two-year rice experiment. The experiments were conducted in 2015 and 2016, involving different N rates, planting densities and rice cultivars, with three replicates. An Oktokopter UAS was used to acquire aerial photography at early growth stages (from tillering to booting) and field samplings were taken at a near date. Two color indices (normalized excess green index (NExG), and normalized green red difference index (NGRDI)), two near infrared vegetation indices (green normalized difference vegetation index (GNDVI), and enhanced NDVI (ENDVI)) and two red edge vegetation indices (red edge chlorophyll index (CIred edge), and DATT) were used to evaluate the capability of these three sensors in estimating leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA) in rice. The results demonstrated that the red edge vegetation indices derived from MS images produced the highest estimation accuracy for LNA (R2: 0.79–0.81, root mean squared error (RMSE): 1.43–1.45 g m−2) and PNA (R2: 0.81–0.84, RMSE: 2.27–2.38 g m−2). The GNDVI from CIR images yielded a moderate estimation accuracy with an all-stage model. Color indices from RGB images exhibited satisfactory performance for the pooled dataset of the tillering and jointing stages. Compared with the counterpart indices from the RGB and CIR images, the indices from the MS images performed better in most cases. These results may set strong foundations for the development of UAS-based rice growth monitoring systems, providing useful information for the real-time decision making on crop N management.
Highlights
Nitrogen (N) plays a key role in crop growth and yield formation
We evaluated three sensors (RGB, color near-infrared (CIR) and MS cameras) onboard Unmanned aerial system (UAS) for the estimation of leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA) in rice
Six vegetation indices (VIs) (NExG, normalized green–red difference index (NGRDI), green normalized difference vegetation index (GNDVI), enhanced NDVI (ENDVI), CIred edge and DATT) computed from the corresponding images were employed to estimate LNA and PNA for individual stages and stage groups, and the established VI-LNA and VI-PNA models were evaluated with the test set
Summary
Nitrogen (N) plays a key role in crop growth and yield formation. Rice (Oryza sativa L.) is one of the largest consumers of N fertilizers [1]. The estimation of leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA) is useful for evaluating crop production capability and predicting grain quality, and supporting diagnosis of N status in crop production. RS data is widely used to monitor crop N status, including satellite imagery [7,8], aerial photographs [9] and canopy reflectance spectra [5,10]. Huang et al [8] used FORMOSAT-2 satellite data to estimate aboveground biomass and plant N uptake at the panicle initiation stage of rice in northeast China. Satellite imagery often provides insufficient spatial resolution to monitor crop growth status for smallholders and is affected by cloud conditions. Manned airborne platforms can be used to obtain imagery with high spatial and temporal resolutions, but they are limited by high operational complexity and costs
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