Proceedings :: International Society of Precision Agriculture

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Franzen, D.W

Johal, G

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Ahmad, A

Aggarwal, V

Saraswat, D

El Gamal, A

Johal, G

Li, D

Miao, Y

Fernández, .G

Kitchen, N.R

Ransom, C.

Bean, G.M

Sawyer, .E

Camberato, J.J

Carter, .R

Ferguson, R.B

Franzen, D.W

Laboski, C.A

Nafziger, E.D

Shanahan, J.F

Topics

Applications of Unmanned Aerial Systems

ISPA Community: Nitrogen

Type

Oral

Year

2022

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Filter results2 paper(s) found.
1. Deep Learning-Based Corn Disease Tracking Using RTK Geolocated UAS Imagery Deep learning-based solutions for precision agriculture have achieved promising results in recent times. Deep learning has been used to accurately classify different disease types and disease severity estimation as an initial stage for developing robust disease management systems. However, tracking the spread of diseases, identifying disease hot spots within cornfields, and notifying farmers using deep learning and UAS imagery remains a critical research gap. Therefore, in this study, high resolution,... A. Ahmad, V. Aggarwal, D. Saraswat, A. El gamal, G. Johal Download paper
2. Developing a Machine Learning and Proximal Sensing-based In-season Site-specific Nitrogen Management Strategy for Corn in the US Midwest Effective in-season site-specific nitrogen (N) management strategies are urgently needed to ensure both food security and sustainable agricultural development. Different active canopy sensor-based precision N management strategies have been developed and evaluated in different parts of the world. Recent studies evaluating several sensor-based N recommendation algorithms across the US Midwest indicated that these locally developed algorithms generally did not perform well when used broadly across... D. Li, Y. Miao, .G. Fernández, N.R. Kitchen, C. . Ransom, G.M. Bean, .E. Sawyer, J.J. Camberato, .R. Carter, R.B. Ferguson, D.W. Franzen, D.W. Franzen, D.W. Franzen, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J.F. Shanahan Download paper

Filter results2 paper(s) found.

1. Deep Learning-Based Corn Disease Tracking Using RTK Geolocated UAS Imagery

Deep learning-based solutions for precision agriculture have achieved promising results in recent times. Deep learning has been used to accurately classify different disease types and disease severity estimation as an initial stage for developing robust disease management systems. However, tracking the spread of diseases, identifying disease hot spots within cornfields, and notifying farmers using deep learning and UAS imagery remains a critical research gap. Therefore, in this study, high resolution,... A. Ahmad, V. Aggarwal, D. Saraswat, A. El gamal, G. Johal

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2. Developing a Machine Learning and Proximal Sensing-based In-season Site-specific Nitrogen Management Strategy for Corn in the US Midwest

Effective in-season site-specific nitrogen (N) management strategies are urgently needed to ensure both food security and sustainable agricultural development. Different active canopy sensor-based precision N management strategies have been developed and evaluated in different parts of the world. Recent studies evaluating several sensor-based N recommendation algorithms across the US Midwest indicated that these locally developed algorithms generally did not perform well when used broadly across... D. Li, Y. Miao, .G. Fernández, N.R. Kitchen, C. . Ransom, G.M. Bean, .E. Sawyer, J.J. Camberato, .R. Carter, R.B. Ferguson, D.W. Franzen, D.W. Franzen, D.W. Franzen, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J.F. Shanahan

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