Proceedings :: International Society of Precision Agriculture

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Scarpin, G.J

Soaud, A.A

Schmidt, J.P

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Authors

Soaud, A.A

Rahman, .M

Al Darwish, F.H

Sripada, R.P

Schmidt, J.P

BHATTARAI, A

Jakhar, A

Bastos, L

Scarpin, G.J

Bastos, L

Fuhrer, L

Porter, W

Scarpin, G.J

Kaur Dhaliwal, A

Bhattarai, A

Jakhar, A

Rorato, A

Zolin, P.

Scarpin, G.J

Deponti, L.P

Echer, F.R

Bastos, L

Dhaliwal, A

Bastos, L

SV, K

Bhattarai, A

Jakhar , A

Poudel, K

Scarpin , G.J

Topics

Precision Nutrient Management

Remote Sensing for Nitrogen Management

Precision Agriculture for Sustainability and Environmental Protection

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

UAV-Based Scouting, Imaging, and Targeted Applications

Type

Poster

Oral

Year

2012

2008

2024

2026

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Filter results6 paper(s) found.
1. Stable Isotope N-15 as Precision Technique to Investigate Elemental Sulfur Effects on Fertilizer Nitrogen Use Efficiency of Corn Grown in Calcareous Sandy Soils ... A.A. Soaud, .M. Rahman, F.H. Al Darwish Download paper
2. Variability in Observed and Sensor Based Estimated Optimum N Rates in Corn Recent research showed that active sensors such as Crop Circle can be used to estimate in-season N requirements for corn. The objective of this research was to identify sources of variability in the observed and Crop Circle-estimated optimum N rates. Field experiments were conducted at two locations for a total of five sites during the 2007 growing season using a randomized complete block design with increasing N rates applied at V6-V8 (NV6) as the treatment factor. Field sites were selected from... R.P. Sripada, J.P. Schmidt 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
4. Comparison of Machine Learning Models for Within-field Cotton Yield Prediction: Assessing the Value of Temporal Data and Generalizability Cotton (Gossypium hirsitum L.) yield prediction is challenging due to the complex interactions between static environmental factors, such as soil properties and topography, and dynamic variables, including weather and crop management. These factors generate significant spatial and temporal variability within and across fields. While remote sensing technologies, particularly satellite-derived vegetation indices, provide spatially explicit data to evaluate crop health, translating... L. Bastos, L. Fuhrer, W. Porter, G.J. Scarpin, A. Kaur Dhaliwal, A. Bhattarai, A. Jakhar Download paper
5. Sensor-based plant growth regulator management in cotton: plot-level and within-plant yield distribution Cotton yield is distributed among canopy thirds, and the use of plant growth regulators (PGRs) modulate this balance, affecting fruiting and yield. Drone-mounted sensors can be used to estimate plant growth and generate maps for variable rate PGR applications to support management. This study compared traditional PGR management with fixed timing and rate to sensor-based management by evaluating PGR application rate and timing. Within-plant yield distribution... A. Rorato, P. . Zolin, G.J. Scarpin, L.P. Deponti, F.R. Echer, L. Bastos Download paper
6. Predicting Pre-harvest Cotton Fiber Quality: An Open-data and Machine Learning Framework Intra-field variability in soil properties and topography, and inter-field variability in weather patterns often leads to inconsistent cotton fiber quality and yield outcomes, posing challenges for growers. This study aims to: (i) predict within-field cotton fiber quality traits based on environmental and soil variables using machine learning models; (ii) identify the most influential environmental drivers (weather, vegetation indices, soil properties, terrain characteristics) affecting cotton...

Filter results6 paper(s) found.

1. Stable Isotope N-15 as Precision Technique to Investigate Elemental Sulfur Effects on Fertilizer Nitrogen Use Efficiency of Corn Grown in Calcareous Sandy Soils

... A.A. Soaud, .M. Rahman, F.H. Al Darwish

Download paper

2. Variability in Observed and Sensor Based Estimated Optimum N Rates in Corn

Recent research showed that active sensors such as Crop Circle can be used to estimate in-season N requirements for corn. The objective of this research was to identify sources of variability in the observed and Crop Circle-estimated optimum N rates. Field experiments were conducted at two locations for a total of five sites during the 2007 growing season using a randomized complete block design with increasing N rates applied at V6-V8 (NV6) as the treatment factor. Field sites were selected from... R.P. Sripada, J.P. Schmidt

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

4. Comparison of Machine Learning Models for Within-field Cotton Yield Prediction: Assessing the Value of Temporal Data and Generalizability

Cotton (Gossypium hirsitum L.) yield prediction is challenging due to the complex interactions between static environmental factors, such as soil properties and topography, and dynamic variables, including weather and crop management. These factors generate significant spatial and temporal variability within and across fields. While remote sensing technologies, particularly satellite-derived vegetation indices, provide spatially explicit data to evaluate crop health, translating... L. Bastos, L. Fuhrer, W. Porter, G.J. Scarpin, A. Kaur Dhaliwal, A. Bhattarai, A. Jakhar

Download paper

5. Sensor-based plant growth regulator management in cotton: plot-level and within-plant yield distribution

Cotton yield is distributed among canopy thirds, and the use of plant growth regulators (PGRs) modulate this balance, affecting fruiting and yield. Drone-mounted sensors can be used to estimate plant growth and generate maps for variable rate PGR applications to support management. This study compared traditional PGR management with fixed timing and rate to sensor-based management by evaluating PGR application rate and timing. Within-plant yield distribution... A. Rorato, P. . Zolin, G.J. Scarpin, L.P. Deponti, F.R. Echer, L. Bastos

Download paper

6. Predicting Pre-harvest Cotton Fiber Quality: An Open-data and Machine Learning Framework

Intra-field variability in soil properties and topography, and inter-field variability in weather patterns often leads to inconsistent cotton fiber quality and yield outcomes, posing challenges for growers. This study aims to: (i) predict within-field cotton fiber quality traits based on environmental and soil variables using machine learning models; (ii) identify the most influential environmental drivers (weather, vegetation indices, soil properties, terrain characteristics) affecting cotton...