Proceedings :: International Society of Precision Agriculture

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Barai, K

Buschermohle, M.J

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Authors

Larson, J.A

Stefanini, M

Lambert, D.M

Yin, X

Boyer, C.N

Varco, J.J

Scharf , P.C

Tubaña, B.S

Dunn, D

Savoy, H.J

Buschermohle, M.J

Tyler, D.D

Barai, K

Ewanik, C

Dhiman, V

Zhang, Y

Hodeghatta, U.R

Zhang, Y

Hodeghatta, U.R

Dhiman, V

Barai, K

Trang, T

Topics

Profitability, Sustainability and Adoption

Proximal and Remote Sensing of Soils and Crops (including Phenotyping)

Data Analytics for Production Ag

Type

Oral

Poster

Year

2016

2024

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Authors

Filter results3 paper(s) found.
1. Net Returns and Production Use Efficiency for Optical Sensing and Variable Rate Nitrogen Technologies in Cotton Production This research evaluated the profitability and N use efficiency of real time on-the-go optical sensing measurements (OPM) and variable-rate technologies (VRT) to manage spatial variability in cotton production in the Mississippi River Basin states of Louisiana, Mississippi, Missouri, and Tennessee. Two forms of OPM and VRT and the existing farmer practice (FP) were used to determine N fertilizer rates applied to cotton on farm fields in the four states. Changes in yields and N rates due to OPM... J.A. Larson, M. Stefanini, D.M. Lambert, X. Yin, C.N. Boyer, J.J. Varco, P.C. Scharf , B.S. Tubaña, D. Dunn, H.J. Savoy, M.J. Buschermohle, D.D. Tyler Download paper
2. 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 Download paper
3. Predicting Water Potentials of Wild Blueberries During Drought Treatment Using Hyperspectral Sensor and Machine Learning Detecting water stress on crops early and accurately is crucial to minimize its impact. This study aims to measure water stress in wild blueberry crops non-destructively by analyzing proximal hyperspectral data. The data collection took place in the summer growing season of 2022. A drought experiment was conducted on wild blueberries in the randomized block design in the greenhouse, incorporating various genotypes and irrigation treatments. Hyperspectral data ( spectral range: 400-1000 nm) using... Y. Zhang, U.R. Hodeghatta, V. Dhiman, K. Barai, T. Trang Download paper

Filter results3 paper(s) found.

1. Net Returns and Production Use Efficiency for Optical Sensing and Variable Rate Nitrogen Technologies in Cotton Production

This research evaluated the profitability and N use efficiency of real time on-the-go optical sensing measurements (OPM) and variable-rate technologies (VRT) to manage spatial variability in cotton production in the Mississippi River Basin states of Louisiana, Mississippi, Missouri, and Tennessee. Two forms of OPM and VRT and the existing farmer practice (FP) were used to determine N fertilizer rates applied to cotton on farm fields in the four states. Changes in yields and N rates due to OPM... J.A. Larson, M. Stefanini, D.M. Lambert, X. Yin, C.N. Boyer, J.J. Varco, P.C. Scharf , B.S. Tubaña, D. Dunn, H.J. Savoy, M.J. Buschermohle, D.D. Tyler

Download paper

2. 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

Download paper

3. Predicting Water Potentials of Wild Blueberries During Drought Treatment Using Hyperspectral Sensor and Machine Learning

Detecting water stress on crops early and accurately is crucial to minimize its impact. This study aims to measure water stress in wild blueberry crops non-destructively by analyzing proximal hyperspectral data. The data collection took place in the summer growing season of 2022. A drought experiment was conducted on wild blueberries in the randomized block design in the greenhouse, incorporating various genotypes and irrigation treatments. Hyperspectral data ( spectral range: 400-1000 nm) using... Y. Zhang, U.R. Hodeghatta, V. Dhiman, K. Barai, T. Trang

Download paper