Study for estimation of rice grain protein contents using hyperspectral data

Suhama, Tomoyuki; Takeda, Tomoi; Onodera, Hirotoshi

doi:10.4287/jsprs.49.358

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…Yao et al [8] found that the sensitive spectral band of leaf nitrogen accumulation is mainly located in the visible and near-infrared (NIR) portions by constructing a normalized difference spectral index (NDSI) and ratio spectral index (RSI) using different band combinations. Some studies that estimate GPC via remote sensing revealed the relationship between crop GPC and spectral indices [9][10][11][12]. Zhang et al [13] used principal component analysis to spectrally downscale rice hyperspectral data.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Monitoring of Rice Grain Protein Content Based on a Multidimensional Euclidean Distance Method

et al. 2022

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Grain protein content (GPC) is an important indicator of nutritional quality of rice. In this study, nitrogen fertilization experiments were conducted to monitor GPC for high-quality Indica rice varieties Meixiangzhan 2 (V1) and Wufengyou 615 (V2) in 2019 and 2020. Three types of parameters, including photosynthetic sensitive vegetation indices (VIs), canopy leaf area index (LAI), and crop plant nitrogen accumulation (PNA), obtained from UAV hyperspectral images were used to estimate rice GPC. Two-dimensional and three-dimensional GPC indices were constructed by combining any two of the three types of parameters and all three, respectively, based on the Euclidean distance method. The R2 and RMSE of the two-dimensional GPC index model for variety V1 at the tillering stage were 0.81 and 0.40% for modeling and 0.95 and 0.38% for validation, and 0.91 and 0.27% for modeling and 0.83 and 0.36% for validation for variety V2. The three-dimensional GPC index model for variety V1 had R2 and RMSE of 0.86 and 0.34% for modeling and 0.78 and 0.45% for validation, and 0.97 and 0.17% for modeling and 0.96 and 0.17% for validation for variety V2 at the panicle initiation stage. At the heading stage, the R2 and RMSE of the three-dimensional model for variety V1 were 0.92 and 0.26% for modeling and 0.91 and 0.37% for validation, and 0.96 and 0.20% for modeling and 0.99 and 0.15% for validation for variety V2. These results demonstrate that the GPC monitoring models incorporating multiple crop growth parameters based on Euclidean distance can improve GPC estimation accuracy and have the potential for field-scale GPC monitoring.

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Section: Introductionmentioning

confidence: 99%

Remote Sensing Monitoring of Rice Grain Protein Content Based on a Multidimensional Euclidean Distance Method

et al. 2022

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“…If remote sensing can be used to estimate GPC, it would be possible not only to determine the spatial distribution of GPC but also to reduce the labor involved in GPC measurement. Previous studies of GPC estimation via remote sensing have clarified the relationship between GPC and spectral indices [24,[26][27][28]. The findings have shown (1) as the vegetation indices in the grain-filling stage increases, GPC increases; (2) the canopy nitrogen content affects spectral reflectance, and GPC can be estimated indirectly; and (3) regression models for GPC estimation must be remade each year.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Protein Concentration in Rice Grain Using UAV Imagery Together with Agroclimatic Data

Hama

Tanaka²,

Tsuruoka

et al. 2020

Agronomy

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Global warming and climate change can potentially change not only rice production but also rice nutrient content. To adapt a rice-dependent country’s farming to the impacts of climate change, it is necessary to assess and monitor the potential risk that climate change poses to agriculture. The aim of this study was to clarify the relationship between rice grain protein content (GPC) and meteorological variables through unmanned aerial vehicle remote sensing and meteorological measurements. Furthermore, a method for GPC estimation that combines remote sensing data and meteorological variables was proposed. The conclusions of this study were as follows: (1) The accuracy and robustness of the GPC estimation model were improved by evaluating the nitrogen condition with the green normalized difference vegetation index at the heading stage (GNDVIheading) and evaluating photosynthesis with the average daily solar radiation during the grain-filling stage (SRgrain-filling). GPC estimation considering SRgrain-filling in addition to GNDVIheading was able to estimate the observed GPC under the different conditions. (2) Increased temperature from the transplantation date to the heading stage can affect increased GPC when extreme temperature does not cause the heat stress.

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Study for estimation of rice grain protein contents using hyperspectral data

Cited by 2 publications

References 10 publications

Remote Sensing Monitoring of Rice Grain Protein Content Based on a Multidimensional Euclidean Distance Method

Remote Sensing Monitoring of Rice Grain Protein Content Based on a Multidimensional Euclidean Distance Method

Estimating the Protein Concentration in Rice Grain Using UAV Imagery Together with Agroclimatic Data

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