Citation for Excellence

 

Teff: Food for the Future

 

2022 AGE EDITOR'S CITATION FOR EXCELLENCE AWARD

 

Growing Conditions Influence Wheat Nitrogen Recommendations

UAV-based NDVI estimation of sugarbeet yield and quality under varied nitrogen and water rates

 

Crop Sensors and UAVs

 

Southwest Idaho, UI Parma 2021-22 Winter Wheat and Barley Variety Performance

 

Southwest Idaho, UI Parma 2022 Spring Wheat and Barley Variety Performance

 

Wheat Yield and Protein Estimation with Handheld and Unmanned Aerial Vehicle-Mounted Sensors.

Wheat Yield and Protein Estimation with Handheld and Unmanned Aerial Vehicle-Mounted Sensors

by 

Olga S. Walsh

 1,*,

Juliet M. Marshall

 2,

Eva Nambi

 1,

Chad A. Jackson

 3,

Emmanuella Owusu Ansah

 1,

Ritika Lamichhane

 1,

Jordan McClintick-Chess

 4 and

Francisco Bautista

 1

1

Parma R&E Center, University of Idaho, Parma, ID 83660, USA

2

Idaho Falls R&E Center, University of Idaho, Idaho Falls, ID 83402, USA

3

Aberdeen R&E Center, University of Idaho, Aberdeen, ID 83210, USA

4

AG Idaho Consulting LLC, Wilder, ID 83676, USA

*

Author to whom correspondence should be addressed.

Agronomy 2023, 13(1), 207; https://doi.org/10.3390/agronomy13010207 (registering DOI)

Received: 11 November 2022 / Revised: 29 December 2022 / Accepted: 2 January 2023 / Published: 10 January 2023

Abstract

Accurate sensor-based prediction of crop yield and grain quality in-season would enable growers to adjust nitrogen (N) fertilizer management for optimized production. This study assessed the feasibility (and compared the accuracy) of wheat (Triticum aestivum L.) yield, grain N uptake, and protein content prediction with in-season crop spectral reflectance measurements (Normalized Difference Vegetative Index, NDVI) obtained with a handheld GreenSeeker (GS) sensor and an Unmanned Aerial Vehicle (UAV)-mounted sensor. A strong positive correlation was observed between GS NDVI and UAV NDVI at Feekes 5 (R2 = 0.78) and Feekes 10 (R2 = 0.70). At Feekes 5, GS NDVI and UAV NDVI explained 42% and 43% of wheat yield, respectively. The correlation was weaker at Feekes 10 (R2 of 0.34 and 0.25 for GS NDVI and UAV NDVI, respectively). The accuracy of wheat grain N uptake prediction was comparable to that of yield: the R2 values for GS NDVI and UAV NDVI were 0.53 and 0.37 at Feekes 5 and 0.13 and 0.20 at Feekes 10. We found that neither GS NDVI nor UAV NDVI in-season data were useful in prediction of wheat grain protein content. In conclusion, wheat yield and grain N uptake can be estimated at Feekes 5 using either handheld or aerial based NDVI with comparable accuracy.

Keywords: 

normalized difference vegetative index; handheld sensor; unmanned aerial vehicle; wheat; grain yield; grain protein; grain N uptake

We hypothesized that the in-season NDVI from handheld (GS) and aerial (UAV) sensors can be used to accurately estimate wheat yield, grain N uptake, and protein content. Our results have confirmed that wheat yield and grain N uptake can be estimated with GS NDVI and UAV NDVI. We observed a strong linear relationship between GS NDVI and UAV NDVI (0.78), especially at tillering. The GS NDVI and UAV NDVI relationship with wheat yield and N uptake was stronger at tillering compared to flowering. This makes sense because at tillering the plant vigor is high and most of N uptake is channeled towards biomass production. In contrast, at the flowering stage, the N uptake direction is shifted towards grain production. The GS NDVI and UAV NDVI correlation with wheat yield and grain N uptake were comparable. We found that neither GS NDVI nor UAV NDVI in-season data were particularly useful in the prediction of wheat grain protein content.

Author Contributions: Conceptualization, O.S.W. and J.M.M.; Methodology, O.S.W. and J.M.-C.; Investigation, J.M.-C. and C.A.J.; Data curation, O.S.W., E.N., R.L., E.O.A. and F.B.; Writing—original draft, E.N.; Writing—review and editing, O.S.W. and E.N.; Visualization, O.S.W.; Supervision, O.S.W. and J.M.-C.; Project administration, O.S.W. and J.M.M.; Funding acquisition, O.S.W. and J.M.M. All authors have read and agreed to the published version of the manuscript.

Funding: This work was funded in part by the Idaho Wheat Commission and the Idaho wheat producers.

Estimation Of Sugar Beet Yield And Quality With UAV-Based Sensors

 

Estimation of Wheat Yield and Grain Protein with Handheld and UAV-Mounted Sensors

 

Ag Talk Tuesday connects with growers and agricultural professionals

 

The rising cost of fertilizers

 

Wheat yield and protein estimation with handheld- and UAV-based reflectance measurements

 Core Ideas

∙ GreenSeeker (GS) and UAV NDVI at Feekes 5 shows the most potential for in-season wheat yield estimation.

∙ Comparable accuracy of yield estimation was achieved with GS NDVI and UAV NDVI.

∙ The accuracy of yield estimation was comparable for all wheat classes and cultivars.

∙ Estimation of in-season wheat protein content remains a challenge.

UI Parma MS student to serve on national Graduate Student Committee

 

Our MS student, Emmanuella Owusu Ansah, a graduate research assistant in the Cropping Systems Agronomy program at UI Parma, has been selected to serve on this committee, and she will also be participating in the Graduate Student Leadership Conference at the tri-societies' annual meeting in Baltimore, MD in November. 

Drones for Fruit Producers