Abstract
Optimizing nitrogen (N) management in rice is crucial for China’s food security and sustainable agricultural development. Nondestructive crop growth monitoring based on remote sensing technologies can accurately assess crop N status, which may be used to guide the in-season site-specific N recommendations. The fixed-wing unmanned aerial vehicle (UAV)-based remote sensing is a low-cost, easy-to-operate technology for collecting spectral reflectance imagery, an important data source for precision N management. The relationships between many vegetation indices (VIs) derived from spectral reflectance data and crop parameters are known to be nonlinear. As a result, nonlinear machine learning methods have the potential to improve the estimation accuracy. The objective of this study was to evaluate five different approaches for estimating rice (Oryza sativa L.) aboveground biomass (AGB), plant N uptake (PNU), and N nutrition index (NNI) at stem elongation (SE) and heading (HD) stages in Northeast China: (1) single VI (SVI); (2) stepwise multiple linear regression (SMLR); (3) random forest (RF); (4) support vector machine (SVM); and (5) artificial neural networks (ANN) regression. The results indicated that machine learning methods improved the NNI estimation compared to VI-SLR and SMLR methods. The RF algorithm performed the best for estimating NNI (R2 = 0.94 (SE) and 0.96 (HD) for calibration and 0.61 (SE) and 0.79 (HD) for validation). The root mean square errors (RMSEs) were 0.09, and the relative errors were <10% in all the models. It is concluded that the RF machine learning regression can significantly improve the estimation of rice N status using UAV remote sensing. The application machine learning methods offers a new opportunity to better use remote sensing data for monitoring crop growth conditions and guiding precision crop management. More studies are needed to further improve these machine learning-based models by combining both remote sensing data and other related soil, weather, and management information for applications in precision N and crop management.
Highlights
Rice (Oryza sativa L.) is one of the most important crops in the world, consumed by more than 60% of China’s population as a staple food
The NIR band consistently had the highest R2 for aboveground biomass (AGB) and plant N uptake (PNU), while for plant N concentration (PNC) and nutrition index (NNI), the sensitivity of different wavebands changed with growth stages
The results of this study indicated that Green Optimized Soil Adjusted Vegetation Index (GOSAVI), Nonlinear Index (NLI), and Modified Green Soil Adjusted Vegetation Index (MGSAVI) performed best, explaining 65%, 65%, and 74% of rice AGB variability at stem elongation (SE), HD, and across growth stages, respectively
Summary
Rice (Oryza sativa L.) is one of the most important crops in the world, consumed by more than 60% of China’s population as a staple food. Rice production in China is a major consumer of nitrogen (N) fertilizers, but the N use efficiency (NUE) is less than 30% [1]. Uniform fertilizer application across the fields according to experience or regional guidelines is the common practice and can lead to over-application of N at low yielding areas. Precision N management (PNM) has the potential to effectively improve NUE, reduce soil and groundwater pollution, and increase farmers’ income [2]. Efficient tools for rapid and in-season diagnosis of rice N status over large areas are essential for the practical implementation of the PNM strategies
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