Proceedings :: International Society of Precision Agriculture

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Upadhayaya, S.K

Trout, T.J

Znoj, E

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Authors

Ahuja, L.R

Saseendran, S.A

Ma, L

Nielsen, D.C

Trout, T.J

Andales, A.A

Hansen, N.C

Upadhayaya, S.K

Udompetaikul, V

Shafii, M.S

Browne, G.T

Kross, A

Kaur, G

Znoj, E

Callegari, D

Sunohara, M

McNairn, H

Lapen, D

Rudy, H

van Vliet, L

Topics

Modelling and Geo-Statistics

Engineering Technologies

Proximal and Remote Sensing of Soil and Crop (including Phenotyping)

Type

Oral

Poster

Year

2008

2018

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Authors

Filter results3 paper(s) found.
1. Use of a Cropping System Model for Soil-specific Optimization of Limited Water In the arena of modern agriculture, system models capable of simulating the complex interactions of all the relevant processes in the soil-water-plant- atmosphere continuum are widely accepted as potential tools for decision support to optimize crop inputs of water to achieve location specific yield potential while minimizing environmental (soil and water resources) impacts. In a recent study, we calibrated, validated, and applied the CERES-Maize v4.0 model for simulating limited-water irrigation... L.R. Ahuja, S.A. Saseendran, L. Ma, D.C. Nielsen, T.J. Trout, A.A. Andales, N.C. Hansen Download paper
2. A Tree Planting Site-Specific Fumigant Applicator for Orchard Crops The goal of this research was to use recent advances in the global positioning system and computer technology to apply just the right amount of fumigant where it is most needed (i.e., in the neighborhood of each tree planting site or tree- planting-site-specific application) to decrease the incidence of replant disease, and achieve the environmental and economical benefits of reducing the application of these toxic chemicals. In the first year of this study we retrofitted a chemical applicator... S.K. Upadhayaya, V. Udompetaikul, M.S. Shafii, G.T. Browne Download paper
3. Evaluation of an Artificial Neural Network Approach for Prediction of Corn and Soybean Yield The ability to predict crop yield during the growing season is important for crop income, insurance projections and for evaluating food security. Yet, modeling crop yield is challenging because of the complexity of the relationships between crop growth and the interrelated predictor variables. Artificial neural networks (ANNs) are useful for such complex systems as they can capture non-linear relationships of data without explicitly knowing the underlying processes. In this study, an ANN-based... A. Kross, G. Kaur, E. Znoj, D. Callegari, M. Sunohara, H. Mcnairn, D. Lapen, H. Rudy, L. Van Vliet Download paper

Filter results3 paper(s) found.

1. Use of a Cropping System Model for Soil-specific Optimization of Limited Water

In the arena of modern agriculture, system models capable of simulating the complex interactions of all the relevant processes in the soil-water-plant- atmosphere continuum are widely accepted as potential tools for decision support to optimize crop inputs of water to achieve location specific yield potential while minimizing environmental (soil and water resources) impacts. In a recent study, we calibrated, validated, and applied the CERES-Maize v4.0 model for simulating limited-water irrigation... L.R. Ahuja, S.A. Saseendran, L. Ma, D.C. Nielsen, T.J. Trout, A.A. Andales, N.C. Hansen

Download paper

2. A Tree Planting Site-Specific Fumigant Applicator for Orchard Crops

The goal of this research was to use recent advances in the global positioning system and computer technology to apply just the right amount of fumigant where it is most needed (i.e., in the neighborhood of each tree planting site or tree- planting-site-specific application) to decrease the incidence of replant disease, and achieve the environmental and economical benefits of reducing the application of these toxic chemicals. In the first year of this study we retrofitted a chemical applicator... S.K. Upadhayaya, V. Udompetaikul, M.S. Shafii, G.T. Browne

Download paper

3. Evaluation of an Artificial Neural Network Approach for Prediction of Corn and Soybean Yield

The ability to predict crop yield during the growing season is important for crop income, insurance projections and for evaluating food security. Yet, modeling crop yield is challenging because of the complexity of the relationships between crop growth and the interrelated predictor variables. Artificial neural networks (ANNs) are useful for such complex systems as they can capture non-linear relationships of data without explicitly knowing the underlying processes. In this study, an ANN-based... A. Kross, G. Kaur, E. Znoj, D. Callegari, M. Sunohara, H. Mcnairn, D. Lapen, H. Rudy, L. Van Vliet

Download paper