Progress of hyperspectral data processing and modelling for cereal crop nitrogen monitoring

Fu, Yuanyuan; Yang, Guijun; Li, Zhenhai; Li, Heli; Li, Zhenhong; Xu, Xingang; Song, Xiaoyu; Zhang, Yunhe; Duan, Dandan; Zhao, Chunjiang; Chen, Liping

doi:10.1016/j.compag.2020.105321

Cited by 32 publications

(17 citation statements)

References 128 publications

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“…Among the sets of sensitive features for the three stages, the red-edge (REG) and near infrared (NIR) band features were significantly selected for LNC estimates; the two features had also been used to predict maize LNC in an existing report [10]. In practice, REG is one of the most widely used spectral features for evaluating crop parameters [39,[55][56][57][58][59] and NIR is also the key component for most typical VIs [16,[31][32][33][34][35][36][37][38][39][40][41][42][43].…”

Section: Vegetation Features For Lnc Evaluatesmentioning

confidence: 99%

Estimating Leaf Nitrogen Content in Corn Based on Information Fusion of Multiple-Sensor Imagery from UAV

Fan

et al. 2021

Remote Sensing

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With the rapid development of unmanned aerial vehicle (UAV) and sensor technology, UAVs that can simultaneously carry different sensors have been increasingly used to monitor nitrogen status in crops due to their flexibility and adaptability. This study aimed to explore how to use the image information combined from two different sensors mounted on an UAV to evaluate leaf nitrogen content (LNC) in corn. Field experiments with corn were conducted using different nitrogen rates and cultivars at the National Precision Agriculture Research and Demonstration Base in China in 2017. Digital RGB and multispectral images were obtained synchronously by UAV in the V12, R1, and R3 growth stages of corn, respectively. A novel family of modified vegetation indices, named coverage adjusted spectral indices (CASIs (CASI =VI/1+FVcover, where VI denotes the reference vegetation index and FVcover refers to the fraction of vegetation coverage), has been introduced to estimate LNC in corn. Thereby, typical VIs were extracted from multispectral images, which have the advantage of relatively higher spectral resolution, and FVcover was calculated by RGB images that feature higher spatial resolution. Then, the PLS (partial least squares) method was employed to investigate the relationships between LNC and the optimal set of CASIs or VIs selected by the RFA (random frog algorithm) in different corn growth stages. The analysis results indicated that whether removing soil noise or not, CASIs guaranteed a better estimation of LNC than VIs for all of the three growth stages of corn, and the usage of CASIs in the R1 stage yielded the best R2 value of 0.59, with a RMSE (root mean square error) of 22.02% and NRMSE (normalized root mean square error) of 8.37%. It was concluded that CASIs, based on the fusion of information acquired synchronously from both lower resolution multispectral and higher resolution RGB images, have a good potential for crop nitrogen monitoring by UAV. Furthermore, they could also serve as a useful way for assessing other physical and chemical parameters in further applications for crops.

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Section: Vegetation Features For Lnc Evaluatesmentioning

confidence: 99%

Estimating Leaf Nitrogen Content in Corn Based on Information Fusion of Multiple-Sensor Imagery from UAV

Fan

et al. 2021

Remote Sensing

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“…After preprocessing, the data processing pipeline is usually sensor specific. 1D spectral curves, such as the hyperspectral curve, usually require dimension reduction ( Luo et al., 2020 ), wavelet transformation ( Paul and Chaki, 2021 ), and spectral index calculation ( Fu et al., 2020 ). 2D image–based phenotyping (e.g., with RGB images or multi-/hyperspectral images) usually involves image registration ( Tondewad and Dale, 2020 ), classification ( Cheng et al., 2020 ), segmentation ( Hossain and Chen, 2019 ), and trait extraction ( Jiang et al., 2020 ).…”

Section: How To Link Prs To G × P × E Studiesmentioning

confidence: 99%

Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

Tao

Tian

et al. 2022

Plant Communications

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“…Among them, kernel-based regression methods [e.g., support vector regression (SVR) and Gaussian process regression (GPR)] use structural risk minimization. Therefore, with a limited training set, these methods are considered to have a better generalization ability than artificial neural networks (ANNs) (Fu et al, 2020). Li et al (2016) compared four chemometric techniques used to estimate N status in winter wheat plants using spectral features.…”

Section: Dissection Of Hyperspectral Reflectance To Estimate Nitrogen...mentioning

confidence: 99%

Applications of a Hyperspectral Imaging System Used to Estimate Wheat Grain Protein: A Review

Zheng

2022

Front. Plant Sci.

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Recent research advances in wheat have focused not only on increasing grain yields, but also on establishing higher grain quality. Wheat quality is primarily determined by the grain protein content (GPC) and composition, and both of these are affected by nitrogen (N) levels in the plant as it develops during the growing season. Hyperspectral remote sensing is gradually becoming recognized as an economical alternative to traditional destructive field sampling methods and laboratory testing as a means of determining the N status within wheat. Currently, hyperspectral vegetation indices (VIs) and linear nonparametric regression are the primary tools for monitoring the N status of wheat. Machine learning algorithms have been increasingly applied to model the nonlinear relationship between spectral data and wheat N status. This study is a comprehensive review of available N-related hyperspectral VIs and aims to inform the selection of VIs under field conditions. The combination of feature mining and machine learning algorithms is discussed as an application of hyperspectral imaging systems. We discuss the major challenges and future directions for evaluating and assessing wheat N status. Finally, we suggest that the underlying mechanism of protein formation in wheat grains as determined by using hyperspectral imaging systems needs to be further investigated. This overview provides theoretical and technical support to promote applications of hyperspectral imaging systems in wheat N status assessments; in addition, it can be applied to help monitor and evaluate food and nutrition security.

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Progress of hyperspectral data processing and modelling for cereal crop nitrogen monitoring

Cited by 32 publications

References 128 publications

Estimating Leaf Nitrogen Content in Corn Based on Information Fusion of Multiple-Sensor Imagery from UAV

Estimating Leaf Nitrogen Content in Corn Based on Information Fusion of Multiple-Sensor Imagery from UAV

Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

Applications of a Hyperspectral Imaging System Used to Estimate Wheat Grain Protein: A Review

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