Advancing on‐farm research with UAVs: Cover crop effects on crop growth and yield

Yuan, Mingjun; Burjel, J.C.; Martín, Nicolas F.; Isermann, J.; Goeser, Nicholas J.; Pittelkow, Cameron M.

doi:10.1002/agj2.20546

Cited by 3 publications

(2 citation statements)

References 56 publications

(138 reference statements)

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“…During these stages, all plots displayed high vegetative vigor and canopy coverage, leading to peak values for the indices. Several authors have reported the saturation of certain VIs, especially NDVI [70], when they reach higher values.…”

Section: Prediction Of Productive Componentsmentioning

confidence: 99%

“…Currently, there is an increasing trend in precision agriculture toward integrating technologies such as multispectral/hyperspectral/RGB cameras, UAV platforms, and IR yield monitoring results to enable high-capacity data analysis. This utilizes high-resolution geospatial datasets to expand the information network to the field [70]. Identifying areas of low, medium, and high yield at the field level and tracking temporal variations over time facilitate targeted management practices, ultimately enhancing profitability [82].…”

Section: Spectral Response Uav To Treatmentsmentioning

confidence: 99%

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Geotechnologies in Biophysical Analysis through the Applicability of the UAV and Sentinel-2A/MSI in Irrigated Area of Common Beans: Accuracy and Spatial Dynamics

de Oliveira,

de Castro,

Sousa

et al. 2024

Remote Sensing

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The applicability of remote sensing enables the prediction of nutritional value, phytosanitary conditions, and productivity of crops in a non-destructive manner, with greater efficiency than conventional techniques. By identifying problems early and providing specific management recommendations in bean cultivation, farmers can reduce crop losses, provide more accurate and adequate diagnoses, and increase the efficiency of agricultural resources. The aim was to analyze the efficiency of vegetation indices using remote sensing techniques from UAV multispectral images and Sentinel-2A/MSI to evaluate the spectral response of common bean (Phaseolus vulgaris L.) cultivation in different phenological stages (V4 = 32 DAS; R5 = 47 DAS; R6 = 60 DAS; R8 = 74 DAS; and R9 = 89 DAS, in 99 days after sowing—DAS) with the application of doses of magnesium (0, 250, 500, and 1000 g ha−1). The field characteristics analyzed were mainly chlorophyll content, productivity, and plant height in an experimental area by central pivot in the midwest region of Brazil. Data from UAV vegetation indices served as variables for the treatments implemented in the field and were statistically correlated with the crop’s biophysical parameters. The spectral response of the bean crop was also detected through spectral indices (NDVI, NDMI_GAO, and NDWI_GAO) from Sentinel-2A/MSI, with spectral resolutions of 10 and 20 m. The quantitative values of NDVI from UAV and Sentinel-2A/MSI were evaluated by multivariate statistical analysis, such as principal components (PC), and cophenetic correlation coefficient (CCC), in the different phenological stages. The NDVI and MCARI vegetation indices stood out for productivity prediction, with r = 0.82 and RMSE of 330 and 329 kg ha−1, respectively. The TGI had the best performance in terms of plant height (r = 0.73 and RMSE = 7.4 cm). The best index for detecting the relative chlorophyll SPAD content was MCARI (r = 0.81; R2 = 0.66 and RMSE = 10.14 SPAD), followed by NDVI (r = 0.81; R2 = 0.65 and RMSE = 10.19 SPAD). The phenological stage with the highest accuracy in estimating productive variables was R9 (Physiological maturation). GNDVI in stages R6 and R9 and VARI in stage R9 were significant at 5% for magnesium doses, with quadratic regression adjustments and a maximum point at 500 g ha−1. Vegetation indices based on multispectral bands of Sentinel-2A/MSI exhibited a spectral dynamic capable of aiding in the management of bean crops throughout their cycle. PCA (PC1 = 48.83% and PC2 = 39.25%) of the satellite multiple regression model from UAV vs. Sentinel-2A/MSI presented a good coefficient of determination (R2 = 0.667) and low RMSE = 0.12. UAV data for the NDVI showed that the Sentinel-2A/MSI samples were more homogeneous, while the UAV samples detected a more heterogeneous quantitative pattern, depending on the development of the crop and the application of doses of magnesium. Results shown denote the potential of using geotechnologies, especially the spectral response of vegetation indices in monitoring common bean crops. Although UAV and Sentinel-2A/MSI technologies are effective in evaluating standards of the common bean crop cycle, more studies are needed to better understand the relationship between field variables and spectral responses.

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Section: Prediction Of Productive Componentsmentioning

confidence: 99%

Section: Spectral Response Uav To Treatmentsmentioning

confidence: 99%

Geotechnologies in Biophysical Analysis through the Applicability of the UAV and Sentinel-2A/MSI in Irrigated Area of Common Beans: Accuracy and Spatial Dynamics

de Oliveira,

de Castro,

Sousa

et al. 2024

Remote Sensing

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The response of a sorghum sudangrass cover crop to residual nitrogen and its relationship with spectral sensors

Netto‐Ferreira,

de Sá Leitão,

Griffin

et al. 2024

Agrosystems Geosci & Env

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A sorghum sudangrass (SSG) cover crop grown after a cash crop could take up residual nitrogen (N) before it is lost. As in‐field monitoring of SSG properties is laborious, predicting biomass and N concentrations with spectral sensors could be useful. At two sites in Live Oak, Florida, we evaluated the response of SSG to residual N from previous N fertilization and the performance of handheld and satellite sensors in estimating SSG properties. We quantified aboveground biomass, plant N, leaf greenness (NDVI), net potential N mineralization (PNM), and soil permanganate oxidizable carbon (POXC). Residual N did not affect SSG properties, PNM was highest at the highest N input rate in one site, and soil POXC was correlated with SSG properties (biomass and plant N). NDVI measured from a handheld sensor better predicted SSG properties than satellite imagery in these small plots, suggesting a greater potential to be a useful management tool.

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Simulating the Leaf Area Index of Rice from Multispectral Images

Liu

Zeng

et al. 2021

Remote Sensing

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Accurate estimation of the leaf area index (LAI) is essential for crop growth simulations and agricultural management. This study conducted a field experiment with rice and measured the LAI in different rice growth periods. The multispectral bands (B) including red edge (RE, 730 nm ± 16 nm), near-infrared (NIR, 840 nm ± 26 nm), green (560 nm ± 16 nm), red (650 nm ± 16 nm), blue (450 nm ± 16 nm), and visible light (RGB) were also obtained by an unmanned aerial vehicle (UAV) with multispectral sensors (DJI-P4M, SZ DJI Technology Co., Ltd.). Based on the bands, five vegetation indexes (VI) including Green Normalized Difference Vegetation Index (GNDVI), Leaf Chlorophyll Index (LCI), Normalized Difference Red Edge Index (NDRE), Normalized Difference Vegetation Index (NDVI), and Optimization Soil-Adjusted Vegetation Index (OSAVI) were calculated. The semi-empirical model (SEM), the random forest model (RF), and the Extreme Gradient Boosting model (XGBoost) were used to estimate rice LAI based on multispectral bands, VIs, and their combinations, respectively. The results indicated that the GNDVI had the highest accuracy in the SEM (R2 = 0.78, RMSE = 0.77). For the single band, NIR had the highest accuracy in both RF (R2 = 0.73, RMSE = 0.98) and XGBoost (R2 = 0.77, RMSE = 0.88). Band combination of NIR + red improved the estimation accuracy in both RF (R2 = 0.87, RMSE = 0.65) and XGBoost (R2 = 0.88, RMSE = 0.63). NDRE and LCI were the first two single VIs for LAI estimation using both RF and XGBoost. However, putting more than one VI together could only increase the LAI estimation accuracy slightly. Meanwhile, the bands + VIs combinations could improve the accuracy in both RF and XGBoost. Our study recommended estimating rice LAI by a combination of red + NIR + OSAVI + NDVI + GNDVI + LCI + NDRE (2B + 5V) with XGBoost to obtain high accuracy and overcome the potential over-fitting issue (R2 = 0.91, RMSE = 0.54).

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Advancing on‐farm research with UAVs: Cover crop effects on crop growth and yield

Cited by 3 publications

References 56 publications

Geotechnologies in Biophysical Analysis through the Applicability of the UAV and Sentinel-2A/MSI in Irrigated Area of Common Beans: Accuracy and Spatial Dynamics

Geotechnologies in Biophysical Analysis through the Applicability of the UAV and Sentinel-2A/MSI in Irrigated Area of Common Beans: Accuracy and Spatial Dynamics

The response of a sorghum sudangrass cover crop to residual nitrogen and its relationship with spectral sensors

Simulating the Leaf Area Index of Rice from Multispectral Images

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