Proceedings :: International Society of Precision Agriculture

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BHATTARAI, A

Taylor, J.A

Zhang, Y

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Authors

Pasquel, D

Roux, S

Tisseyre, B

Taylor, J.A

Barai, K

Ewanik, C

Dhiman, V

Zhang, Y

Hodeghatta, U.R

BHATTARAI, A

Jakhar, A

Bastos, L

Scarpin, G.J

Topics

Geospatial Data

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

Precision Agriculture for Sustainability and Environmental Protection

Type

Oral

Poster

Year

2022

2024

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Filter results3 paper(s) found.
1. Comparison of Different Aspatial and Spatial Indicators to Assess Performance of Spatialized Crop Models at Different Within-field Scales Most current crop models are point-based models, i.e. they simulate agronomic variables on a spatial footprint on which they were initially designed (e.g. plant, field, region scale). To assess their performances, many indicators based on the comparison of estimated vs observed data, can be used such as root mean square error (RMSE) or Willmott index of agreement (D-index) among others. However, shifting model use from a strategic objective to tactical in-season management is becoming a significant... D. Pasquel, S. Roux, B. Tisseyre, J.A. Taylor 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. Comparing Proximal and Remote Sensors for Variable Rate Nitrogen Management in Cotton Sensing and variable rate technology are becoming increasingly important in precision agriculture. These technologies utilize sensors to monitor crop growth and health, enabling informed decisions such as diagnosing nitrogen (N) stress and applying variable rates of N. Sensor-based solutions allow for customized N applications based on plant needs and environmental factors. This approach has led to notable reductions in N application rates, minimized N losses by improving N use efficiency (NUE),... A. Bhattarai, A. Jakhar, L. Bastos, G.J. Scarpin Download paper

Filter results3 paper(s) found.

1. Comparison of Different Aspatial and Spatial Indicators to Assess Performance of Spatialized Crop Models at Different Within-field Scales

Most current crop models are point-based models, i.e. they simulate agronomic variables on a spatial footprint on which they were initially designed (e.g. plant, field, region scale). To assess their performances, many indicators based on the comparison of estimated vs observed data, can be used such as root mean square error (RMSE) or Willmott index of agreement (D-index) among others. However, shifting model use from a strategic objective to tactical in-season management is becoming a significant... D. Pasquel, S. Roux, B. Tisseyre, J.A. Taylor

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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. Comparing Proximal and Remote Sensors for Variable Rate Nitrogen Management in Cotton

Sensing and variable rate technology are becoming increasingly important in precision agriculture. These technologies utilize sensors to monitor crop growth and health, enabling informed decisions such as diagnosing nitrogen (N) stress and applying variable rates of N. Sensor-based solutions allow for customized N applications based on plant needs and environmental factors. This approach has led to notable reductions in N application rates, minimized N losses by improving N use efficiency (NUE),... A. Bhattarai, A. Jakhar, L. Bastos, G.J. Scarpin

Download paper