Proceedings :: International Society of Precision Agriculture

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Zhao, X

Tietje, R

Belec, C

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Authors

Vigneault, P

Tremblay, N

Bouroubi, M.Y

Belec, C

Fallon, E

Lu, J

Chen, Z

Miao, Y

Li, Y

Zhang, Y

Zhao, X

Jia, M

Fulton, J.P

Wilson, D

Tietje, R

Hawkins, E

Topics

Applications of UAVs (unmanned aircraft vehicle systems) in precision agriculture

In-Season Nitrogen Management

On Farm Experimentation with Site-Specific Technologies

Type

Oral

Year

2014

2022

2024

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Authors

Filter results3 paper(s) found.
1. A Comparison Of Performance Between UAV And Satellite Imagery For N Status Assessment In Corn A number of platforms are available for the sensing of crop conditions. They vary from proximal (tractor-mounted) to satellites orbiting the Earth. A lot of interest has recently emerged from the access to unmanned aerial vehicles (UAVs) or drones that are able to carry sensors payloads providing data at very high spatial resolution. This study aims at comparing the performance of a UAV and satellite imagery acquired over a corn nitrogen response trial set-up. The nitrogen (N) response... P. Vigneault, N. Tremblay, M.Y. Bouroubi, C. Bélec, E. Fallon Download paper
2. In-season Diagnosis of Winter Wheat Nitrogen Status Based on Rapidscan Sensor Using Machine Learning Coupled with Weather Data Nitrogen nutrient index (NNI) is widely used as a good indicator to evaluate the N status of crops in precision farming. However, interannual variation in weather may affect vegetation indices from sensors used to estimate NNI and reduce the accuracy of N diagnostic models. Machine learning has been applied to precision N management with unique advantages in various variables analysis and processing. The objective of this study is to improve the N status diagnostic model for winter wheat by combining... J. Lu, Z. Chen, Y. Miao, Y. Li, Y. Zhang, X. Zhao, M. Jia Download paper
3. Creating Value from On-farm Research: Efields Data Workflow and Management Successes and Challenges Farm operations today generate a large amount of data that can be difficult to properly manage. This challenge is further compounded when conducting on-farm research. The Ohio State University eFields program partners with farmers to conduct on-farm research and share results in a timely manner. Since 2017, the team has conducted and shared 987 trials across Ohio with the annual number of trials increasing from 45 to 292. This rapid increase has required development of a data workflow that streamlines... J.P. Fulton, D. Wilson, R. Tietje, E. Hawkins

Filter results3 paper(s) found.

1. A Comparison Of Performance Between UAV And Satellite Imagery For N Status Assessment In Corn

A number of platforms are available for the sensing of crop conditions. They vary from proximal (tractor-mounted) to satellites orbiting the Earth. A lot of interest has recently emerged from the access to unmanned aerial vehicles (UAVs) or drones that are able to carry sensors payloads providing data at very high spatial resolution. This study aims at comparing the performance of a UAV and satellite imagery acquired over a corn nitrogen response trial set-up. The nitrogen (N) response... P. Vigneault, N. Tremblay, M.Y. Bouroubi, C. Bélec, E. Fallon

Download paper

2. In-season Diagnosis of Winter Wheat Nitrogen Status Based on Rapidscan Sensor Using Machine Learning Coupled with Weather Data

Nitrogen nutrient index (NNI) is widely used as a good indicator to evaluate the N status of crops in precision farming. However, interannual variation in weather may affect vegetation indices from sensors used to estimate NNI and reduce the accuracy of N diagnostic models. Machine learning has been applied to precision N management with unique advantages in various variables analysis and processing. The objective of this study is to improve the N status diagnostic model for winter wheat by combining... J. Lu, Z. Chen, Y. Miao, Y. Li, Y. Zhang, X. Zhao, M. Jia

Download paper

3. Creating Value from On-farm Research: Efields Data Workflow and Management Successes and Challenges

Farm operations today generate a large amount of data that can be difficult to properly manage. This challenge is further compounded when conducting on-farm research. The Ohio State University eFields program partners with farmers to conduct on-farm research and share results in a timely manner. Since 2017, the team has conducted and shared 987 trials across Ohio with the annual number of trials increasing from 45 to 292. This rapid increase has required development of a data workflow that streamlines... J.P. Fulton, D. Wilson, R. Tietje, E. Hawkins