Proceedings :: International Society of Precision Agriculture

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Dill, T

Gaviraghi, R

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Amado, T.J

Santi, A.L

Corassa, G.M

Bisognin, M.B

Gaviraghi, R

Pires, J.L

Waltz, L

Katari, S

Khanal, S

Dill, T

Porter, C

Ortez, O

Lindsey, L

Nandi, A

Waltz, L

Khanal, S

Katari, S

Hong, C

Anup, A

Colbert, J

Potlapally, A

Dill, T

Porter, C

Engle, J

Stewart, C

Subramoni, H

Machiraju, R

Ortez, O

Lindsey, L

Nandi, A

Topics

Spatial Variability in Crop, Soil and Natural Resources

Artificial Intelligence (AI) in Agriculture

Type

Poster

Oral

Year

2016

2024

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Filter results3 paper(s) found.
1. Response of Soybean Cultivars According to Management Zones in Southern Brazil The positioning of soybean cultivars on fields according your environmental response is new strategy to obtain high soybean yields. The aim of this study was to investigate the agronomic response of six soybean cultivars according management zones in Southern Brazil. The study was conducted in 2013/2014 and in two fields located in Boa Vista das Missões, Rio Grande do Sul, Brazil. The experimental design was a randomized complete block in a factorial arrangement (3x6), with three management... T.J. Amado, A.L. Santi, G.M. Corassa, M.B. Bisognin, R. Gaviraghi, J.L. Pires Download paper
2. A Growth Stage Centric Approach to Field Scale Corn Yield Estimation by Leveraging Machine Learning Methods from Multimodal Data Field scale yield estimation is labor-intensive, typically limited to a few samples in a given field, and often happens too late to inform any in-season agronomic treatments. In this study, we used meteorological data including growing degree days (GDD), photosynthetic active radiation (PAR), and rolling average of rainfall combined with hybrid relative maturity, organic matter, and weekly growth stage information from three small-plot research locations... L. Waltz, S. Katari, S. Khanal, T. Dill, C. Porter, O. Ortez, L. Lindsey, A. Nandi Download paper
3. Cyberinfrastructure for Machine Learning Applications in Agriculture: Experiences, Analysis, and Vision Advancements in machine learning algorithms and GPU computational speeds over the last decade have led to remarkable progress in the capabilities of machine learning. This progress has been so much that, in many domains, including agriculture, access to sufficiently diverse and high-quality datasets has become a limiting factor. While many agricultural use cases appear feasible with current compute resources and machine learning algorithms, the lack of software infrastructure for collecting,... L. Waltz, S. Khanal, S. Katari, C. Hong, A. Anup, J. Colbert, A. Potlapally, T. Dill, C. Porter, J. Engle, C. Stewart, H. Subramoni, R. Machiraju, O. Ortez, L. Lindsey, A. Nandi Download paper

Filter results3 paper(s) found.

1. Response of Soybean Cultivars According to Management Zones in Southern Brazil

The positioning of soybean cultivars on fields according your environmental response is new strategy to obtain high soybean yields. The aim of this study was to investigate the agronomic response of six soybean cultivars according management zones in Southern Brazil. The study was conducted in 2013/2014 and in two fields located in Boa Vista das Missões, Rio Grande do Sul, Brazil. The experimental design was a randomized complete block in a factorial arrangement (3x6), with three management... T.J. Amado, A.L. Santi, G.M. Corassa, M.B. Bisognin, R. Gaviraghi, J.L. Pires

Download paper

2. A Growth Stage Centric Approach to Field Scale Corn Yield Estimation by Leveraging Machine Learning Methods from Multimodal Data

Field scale yield estimation is labor-intensive, typically limited to a few samples in a given field, and often happens too late to inform any in-season agronomic treatments. In this study, we used meteorological data including growing degree days (GDD), photosynthetic active radiation (PAR), and rolling average of rainfall combined with hybrid relative maturity, organic matter, and weekly growth stage information from three small-plot research locations... L. Waltz, S. Katari, S. Khanal, T. Dill, C. Porter, O. Ortez, L. Lindsey, A. Nandi

Download paper

3. Cyberinfrastructure for Machine Learning Applications in Agriculture: Experiences, Analysis, and Vision

Advancements in machine learning algorithms and GPU computational speeds over the last decade have led to remarkable progress in the capabilities of machine learning. This progress has been so much that, in many domains, including agriculture, access to sufficiently diverse and high-quality datasets has become a limiting factor. While many agricultural use cases appear feasible with current compute resources and machine learning algorithms, the lack of software infrastructure for collecting,... L. Waltz, S. Khanal, S. Katari, C. Hong, A. Anup, J. Colbert, A. Potlapally, T. Dill, C. Porter, J. Engle, C. Stewart, H. Subramoni, R. Machiraju, O. Ortez, L. Lindsey, A. Nandi

Download paper