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Evers, B
Ewanik, C
El-Sayed, S
Isaksson, T
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Authors
Isaksson, T
Korsaeth, A
Øvergaard, S
El-Sayed, S
Schmidhalter, U
Mistele, B
Evers, B
Rekhi, M
Hettiarachchi, G
Welch, S
Fritz, A
Alderman, P.D
Poland, J
Barai, K
Ewanik, C
Dhiman, V
Zhang, Y
Hodeghatta, U.R
Topics
Remote Sensing Applications in Precision Agriculture
Proximal Sensing in Precision Agriculture
Geospatial Data
Proximal and Remote Sensing of Soils and Crops (including Phenotyping)
Type
Poster
Year
2012
2022
2024
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Filter results4 paper(s) found.

1. Remote NIR-Sensor Fusion with Weather Data for Improved Prediction of Wheat Yield Models

Prediction models for grain yield based on remote sensing data are commonly shown to perform reasonably well for one single cropping season. The model performances often drop, however, when data from more years is included. This may be caused by biased data, resulting from diverging growth conditions from year to year, which affects... T. Isaksson, A. Korsaeth, S. Øvergaard

2. Assessing Water Status in Wheat under Field Conditions Using Laser-Induced Chlorophyll Fluorescence and Hyperspectral Measurements

Classical measurements for estimating water status in plants using oven drying or pressure chambers are tedious and time-consuming. In the field, changes in radiation conditions may further influence the measurements and thus require... S. El-sayed, U. Schmidhalter, B. Mistele

3. Using On-the-Go Soil Sensors to Assess Spatial Variability within the KS Wheat Breeding Program

In plant breeding the impacts of genotype by environment interactions and the challenges to quantify these interactions has long been recognized. Both macro and microenvironment variations in precipitation, temperature and soil nutrient availability have been shown to impact breeder selections. Traditionally, breeders mitigate these interactions by evaluating genotype performance across varying environments over multiple years. However, limitations in labor, equipment and seed availably can limit... B. Evers, M. Rekhi, G. Hettiarachchi, S. Welch, A. Fritz, P.D. Alderman, J. Poland

4. Airborne Spectral Detection of Leaf Chlorophyll Concentration in Wild Blueberries

Leaf chlorophyll concentration (LCC) detection is crucial for monitoring crop physiological status, assessing the overall health of crops, and estimating their photosynthetic potential. Fast, non-destructive, and spatially extensive monitoring of LCC in crops is critical for accurately diagnosing and assessing crop health in large commercial fields. Advancements in hyperspectral remote sensing offer non-destructive and spatially extensive alternatives for monitoring plant parameters such as LCC.... K. Barai, C. Ewanik, V. Dhiman, Y. Zhang, U.R. Hodeghatta