Proceedings :: International Society of Precision Agriculture

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Katari, S

Smith, A

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Authors

Ortiz, B

Perry, C

Sullivan, D.G

Kemerait, R.C

Davis, R.F

Lu, P

Smith, A

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

Precision Conservation

Artificial Intelligence (AI) in Agriculture

Type

Oral

Year

2010

2024

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Filter results3 paper(s) found.
1. Variable Rate Application Of Nematicides On Cotton Fields: A Promising Site-specific Management Strategy The impact of two nematicides [ 1,3 – Dichloropropene (Telone® II) and Aldicarb (Temik)] applied at two rates on RKN population density and cotton (Gossypium hirsutum L.) lint yield were compared across previously determined RKN management zones (MZ) in commercial fields between 2007 and 2009. The MZ were delineated using fuzzy clustering of various surrogate data for soil texture. All treatments were randomly allocated among... B. Ortiz, C. Perry, D.G. Sullivan, R.C. Kemerait, R.F. Davis, P. Lu, A. Smith 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. Variable Rate Application Of Nematicides On Cotton Fields: A Promising Site-specific Management Strategy

The impact of two nematicides [ 1,3 – Dichloropropene (Telone® II) and Aldicarb (Temik)] applied at two rates on RKN population density and cotton (Gossypium hirsutum L.) lint yield were compared across previously determined RKN management zones (MZ) in commercial fields between 2007 and 2009. The MZ were delineated using fuzzy clustering of various surrogate data for soil texture. All treatments were randomly allocated among... B. Ortiz, C. Perry, D.G. Sullivan, R.C. Kemerait, R.F. Davis, P. Lu, A. Smith

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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