Proceedings :: International Society of Precision Agriculture

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Fasso, W

Aggarwal, V

Mei, H

Teboh, J

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Authors

Fu, W

Meng, Z

Wu, G

Dong, J

Mei, H

Zhao, C

Sharma, L

Bu, H

Ashley, R

Endres, G

Teboh, J

Franzen, D.W

Trotter, M

Gregory, S

Trotter, T

Acuna, T

Swain, D

Fasso, W

Roberts, J

Zikan, A

Cosby, A.M

Ahmad, A

Aggarwal, V

Saraswat, D

El Gamal, A

Johal, G

Topics

Engineering Technologies and Advances

Sensor Application in Managing In-season CropVariability

Agricultural Education

Applications of Unmanned Aerial Systems

Type

Poster

Oral

Year

2012

2014

2016

2022

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Filter results4 paper(s) found.
1. Study on Monitoring System of Wheat Sowing In order to real-time monitoring the sowing status of the multi-channel seeder, a distributed monitoring system is developed. The monitoring module of sowing and the monitoring terminal is designed with ... W. Fu, Z. Meng, G. Wu, J. Dong, H. Mei, C. Zhao Download paper
2. Active Optical Sensor Algorithms For Corn Yield Prediction And In-Season N Application In North Dakota A recent series of seventy seven field N rate experiments with corn (Zea mays, L.) in North Dakota was conducted. Multiple regression analysis of the characteristics of the data set indicated that segregating the data into those with high clay soils and those with medium textures increased the relationship between N rate and corn yield. However, the nearly linear positive slope relationship in high clay soils and coarser texture soils with lower yield productivity indicated... L. Sharma, H. Bu, R. Ashley, G. Endres, J. Teboh, D.W. Franzen Download paper
3. SMARTfarm Learning Hub: Next Generation Precision Agriculture Technologies for Agricultural Education The industry demands on higher education agricultural students are rapidly changing. New precision agriculture technologies are revolutionizing the farming industry but the education sector is failing to keep pace. This paper reports on the development of a key resource, the SMARTfarm Learning Hub (www.smartfarmhub.com) that will increase the skill base of higher education students using a range of new agricultural technologies and innovations. The Hub is a world first; it links real industry... M. Trotter, S. Gregory, T. Trotter, T. Acuna, D. Swain, W. Fasso, J. Roberts, A. Zikan, A. Cosby Download paper
4. 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

Filter results4 paper(s) found.

1. Study on Monitoring System of Wheat Sowing

In order to real-time monitoring the sowing status of the multi-channel seeder, a distributed monitoring system is developed. The monitoring module of sowing and the monitoring terminal is designed with ... W. Fu, Z. Meng, G. Wu, J. Dong, H. Mei, C. Zhao

Download paper

2. Active Optical Sensor Algorithms For Corn Yield Prediction And In-Season N Application In North Dakota

A recent series of seventy seven field N rate experiments with corn (Zea mays, L.) in North Dakota was conducted. Multiple regression analysis of the characteristics of the data set indicated that segregating the data into those with high clay soils and those with medium textures increased the relationship between N rate and corn yield. However, the nearly linear positive slope relationship in high clay soils and coarser texture soils with lower yield productivity indicated... L. Sharma, H. Bu, R. Ashley, G. Endres, J. Teboh, D.W. Franzen

Download paper

3. SMARTfarm Learning Hub: Next Generation Precision Agriculture Technologies for Agricultural Education

The industry demands on higher education agricultural students are rapidly changing. New precision agriculture technologies are revolutionizing the farming industry but the education sector is failing to keep pace. This paper reports on the development of a key resource, the SMARTfarm Learning Hub (www.smartfarmhub.com) that will increase the skill base of higher education students using a range of new agricultural technologies and innovations. The Hub is a world first; it links real industry... M. Trotter, S. Gregory, T. Trotter, T. Acuna, D. Swain, W. Fasso, J. Roberts, A. Zikan, A. Cosby

Download paper

4. 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