High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery

Devia, Carlos Andrés; Rojas, Juan; Petro, Eliel; Martínez, Carol; Mondragón, Iván F.; Patiño, Diego; Rebolledo, María Camila; Colorado, Julián

doi:10.1007/s10846-019-01001-5

Cited by 64 publications

(79 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The result could be ascribed to the fact that the data were segmented into too many groups (number of groups = 36) when using CSP as random effects. Therefore, the number of points per group was very low (i.e., [12][13][14][15][16][17][18][19][20][21][22][23][24] and the modelling performance within the groups suffered from randomness and strong uncertainty. Even though the LME model (using CSP) achieved good performance over all data, this approach would lose statistical significance and simply yield the mean for the group.…”

Section: Comparison Of Lme Models With Different Random Effectsmentioning

confidence: 99%

“…In recent years, UAV-based multispectral images were used to estimate the rice growth status and predict grain yield. For example, Devia et al used seven vegetation indices, combined with multivariable regressions to monitor agronomy parameters at different rice growth stages [15]. Duan et al integrated UAV-based vegetation indices (VIs) and abundance information obtained from spectral mixture analysis to improve the prediction accuracy of rice yield at the heading stage [16].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of Rice Growth Parameters Based on Linear Mixed-Effect Model Using Multispectral Images from Fixed-Wing Unmanned Aerial Vehicles

et al. 2019

View full text Add to dashboard Cite

The accurate estimation of aboveground biomass (AGB) and leaf area index (LAI) is critical to characterize crop growth status and predict grain yield. Unmanned aerial vehicle (UAV) -based remote sensing has attracted significant interest due to its high flexibility and easiness of operation. The mixed effect model introduced in this study can capture secondary factors that cannot be captured by standard empirical relationships. The objective of this study was to explore the potential benefit of using a linear mixed-effect (LME) model and multispectral images from a fixed-wing UAV to estimate both AGB and LAI of rice. Field experiments were conducted over two consecutive years (2017–2018), that involved different N rates, planting patterns and rice cultivars. Images were collected by a compact multispectral camera mounted on a fixed-wing UAV during key rice growth stages. LME, simple regression (SR), artificial neural networks (ANN) and random forests (RF) models were developed relating growth parameters (AGB and LAI) to spectral information. Cultivar (C), growth stage (S) and planting pattern (P) were selected as candidates of random effects for the LME models due to their significant effects on rice growth. Compared to other regression models (SR, ANN and RF), the LME model improved the AGB estimation accuracy for all stage groups to varying degrees: the R2 increased by 0.14–0.35 and the RMSE decreased by 0.88–1.80 t ha−1 for the whole season, the R2 increased by 0.07–0.15 and the RMSE decreased by 0.31–0.61 t ha−1 for pre-heading stages and the R2 increased by 0.21–0.53 and the RMSE decreased by 0.72–1.52 t ha−1 for post-heading stages. Further analysis suggested that the LME model also successfully predicted within the groups when the number of groups was suitable. More importantly, depending on the availability of C, S, P or combinations thereof, mixed effects could lead to an outperformance of baseline retrieval methods (SR, ANN or RF) due to the inclusion of secondary effects. Satisfactory results were also obtained for the LAI estimation while the superiority of the LME model was not as significant as that for AGB estimation. This study demonstrates that the LME model could accurately estimate rice AGB and LAI and fixed-wing UAVs are promising for the monitoring of the crop growth status over large-scale farmland.

show abstract

Section: Comparison Of Lme Models With Different Random Effectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Rice Growth Parameters Based on Linear Mixed-Effect Model Using Multispectral Images from Fixed-Wing Unmanned Aerial Vehicles

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The reflectance of a given wavelength provides useful information about leafplant health. The vegetation indices are numbers that are computed from different wavelength reflectances by well-known equations that use the light reflectance of the plants in different bandwidths, especially the green, red, and near infrared (Devia et al 2019). Vegetation indices have been widely used to estimate biomass by using empirical relationships with biomass (Foody et al 2003).…”

Section: Uav Lidar Point Cloud Datamentioning

confidence: 99%

Peer Review #2 of "Estimation of maize above-ground biomass based on stem-leaf separation strategy integrated with LiDAR and optical remote sensing data (v0.2)"

2019

View full text Add to dashboard Cite

Above-ground biomass (AGB) is an important indicator for effectively assessing crop growth and yield and, in addition, is an important ecological indicator for assessing the efficiency with which crops use light and store carbon in ecosystems. However, most existing methods using optical remote sensing to estimate AGB cannot observe structures below the maize canopy, which may lead to poor estimation accuracy. This paper proposes to use the stem-leaf separation strategy integrated with unmanned aerial vehicle LiDAR and multispectral image data to estimate the AGB in maize. First, the correlation matrix was used to screen optimal the LiDAR structural parameters (LSPs) and the spectral vegetation indices (SVIs). According to the screened indicators, the SVIs and the LSPs were subjected to multivariable linear regression (MLR) with the above-ground leaf biomass (AGLB) and above-ground stem biomass (AGSB), respectively. At the same time, all SVIs derived from multispectral data and all LSPs derived from LiDAR data were subjected to partial least squares regression (PLSR) with the AGLB and AGSB, respectively. Finally, the

show abstract

“…The coefficient of determination betwixt the ground truth and the outcomes of this method were found to be 0.98. Carlos A. Devia et al [50] bestowed a high-throughput technique for AGBE (Above ground estimation of biomass) in rice utilizing multispectral NIR (near-infrared) imagery clicked at disparate scales of the crop. By creating an integrated aerial crop monitoring solution (IACMS) utilizing a UAV (Unmanned Aerial Vehicle), this method computes 7 VI that were combined as multi-variable regressions relying on the rice growth phases: vegetative, ripening or reproductive.…”

Section: Review On Crop Condition Monitoring Systemsmentioning

confidence: 99%

Crop Detection and Classification using Remote Sensing Images

2019

IJITEE

View full text Add to dashboard Cite

crop type identification timely and accurately is one of the applications of remote sensing (RS). It assists the people to regulate the variations in the costs of the food grains. RS images are utmost beneficial for agricultural productions. Recent research methodologies focuses mainly on the crops classification using satellite RS image. This paper proffers the survey on crop detection and classification utilizing RS images. This paper also highlights the latest studies regarding the implementation of crop detection and classification techniques like, review on disparate methodologies for crop recognition and classification (different classifiers are used to detect the crop), review on crop conditions monitoring system, and review on identification of yield estimation , crop region, and also crop growth. At last, the performances of the state-of-art methods are contrasted centered on the Kappa coefficient metrics and overall accuracy. Here, accuracy is the notable metric in the crop identification system.

show abstract

High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery

Cited by 64 publications

References 23 publications

Estimation of Rice Growth Parameters Based on Linear Mixed-Effect Model Using Multispectral Images from Fixed-Wing Unmanned Aerial Vehicles

Estimation of Rice Growth Parameters Based on Linear Mixed-Effect Model Using Multispectral Images from Fixed-Wing Unmanned Aerial Vehicles

Peer Review #2 of "Estimation of maize above-ground biomass based on stem-leaf separation strategy integrated with LiDAR and optical remote sensing data (v0.2)"

Crop Detection and Classification using Remote Sensing Images

Contact Info

Product

Resources

About