Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery
Open Access
- 14 April 2021
- journal article
- research article
- Published by MDPI AG in Remote Sensing
- Vol. 13 (8), 1508
- https://doi.org/10.3390/rs13081508
Abstract
Unmanned aerial vehicle-based multispectral imagery including five spectral bands (blue, green, red, red-edge, and near-infrared) for a rice field in the ripening stage was used to develop regression models for predicting the rice yield and protein content and to select the most suitable regression analysis method for the year-invariant model: partial least squares regression, ridge regression, and artificial neural network (ANN). The regression models developed with six vegetation indices (green normalization difference vegetation index (GNDVI), normalization difference red-edge index (NDRE), chlorophyll index red edge (CIrededge), difference NIR/Green green difference vegetation index (GDVI), green-red NDVI (GRNDVI), and medium resolution imaging spectrometer terrestrial chlorophyll index (MTCI)), calculated from the spectral bands, were applied to single years (2018, 2019, and 2020) and multiple years (2018 + 2019, 2018 + 2020, 2019 + 2020, and all years). The regression models were cross-validated through mutual prediction against the vegetation indices in nonoverlapping years, and the prediction errors were evaluated via root mean squared error of prediction (RMSEP). The ANN model was reproducible, with low and sustained prediction errors of 24.2 kg/1000 m2 ≤ RMSEP ≤ 59.1 kg/1000 m2 in rice yield and 0.14% ≤ RMSEP ≤ 0.28% in rice-protein content in all single-year and multiple-year analyses. When the importance of each vegetation index of the regression models was evaluated, only the ANN model showed the same ranking in the vegetation index of the first (MTCI in both rice yield and protein content) and second importance (CIrededge in rice yield and GRNDVI in rice-protein content). Overall, this means that the ANN model has the highest potential for developing a year-invariant model with stable RMSEP and consistent variable ranking.Keywords
Funding Information
- Gyeongnam agricultural research & extension services (LP003114022020)
This publication has 49 references indexed in Scilit:
- Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA's-AVHRRPLOS ONE, 2013
- Infrared Spectroscopy—Enabling an Evidence-Based Diagnostic Surveillance Approach to Agricultural and Environmental Management in Developing CountriesJournal of Near Infrared Spectroscopy, 2007
- Spectral imaging: Principles and applicationsCytometry Part A, 2006
- A neural network integrated approach for rice crop monitoringInternational Journal of Remote Sensing, 2006
- Starch Properties of Mutant Rice High in Resistant StarchJournal of Agricultural and Food Chemistry, 2005
- The MERIS terrestrial chlorophyll indexInternational Journal of Remote Sensing, 2004
- Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leavesJournal of Plant Physiology, 2003
- Analysis of Spectral Measurements in Paddy Field for Predicting Rice Growth and Yield Based on a Simple Crop Simulation ModelPlant Production Science, 1998
- Use of a green channel in remote sensing of global vegetation from EOS-MODISRemote Sensing of Environment, 1996
- Red and photographic infrared linear combinations for monitoring vegetationRemote Sensing of Environment, 1979