Proceedings :: International Society of Precision Agriculture

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Waltz, L

Abdelghafour, F.Y

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Authors

Abdelghafour, F.Y

Rosu, R

Keresztes, B

Germain, C

Da Costa, J

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

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

Artificial Intelligence (AI) in Agriculture

Type

Oral

Year

2018

2024

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Filter results3 paper(s) found.
1. Joint Structure and Colour Based Parametric Classification of Grapevine Organs from Proximal Images Through Several Critical Phenological Stages Proximal colour imaging is the most time and cost-effective automated technology to acquire high-resolution data describing accurately the trellising plane of grapevine. The available textural information is meaningful enough to provide altogether the assessment of additional agronomic parameters that are still estimated either manually or with dedicated and expensive instrumentations. This paper proposes a new framework for the classification of the different organs visible in the trellising... F.Y. Abdelghafour, R. Rosu, B. Keresztes, C. Germain, J. Da costa 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. Joint Structure and Colour Based Parametric Classification of Grapevine Organs from Proximal Images Through Several Critical Phenological Stages

Proximal colour imaging is the most time and cost-effective automated technology to acquire high-resolution data describing accurately the trellising plane of grapevine. The available textural information is meaningful enough to provide altogether the assessment of additional agronomic parameters that are still estimated either manually or with dedicated and expensive instrumentations. This paper proposes a new framework for the classification of the different organs visible in the trellising... F.Y. Abdelghafour, R. Rosu, B. Keresztes, C. Germain, J. Da costa

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