Proceedings :: International Society of Precision Agriculture

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Mullen, R.W

Bugnet, P

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Authors

Mullen, R.W

Phillips, S.B

Raun, W.R

Thomason, W.E

Bouroubi, Y

Bugnet, P

Nguyen-Xuan, T

Bélec, C

Longchamps, L

Vigneault, P

Gosselin, C

Topics

Remote Sensing for Nitrogen Management

Big Data, Data Mining and Deep Learning

Type

Oral

Year

2008

2018

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Filter results2 paper(s) found.
1. Developing Nitrogen Algorithms for Corn Production Using Optical Sensors Remote sensing for nitrogen management in cereal crops has been an intensive research area due to environmental concerns and economic realities of today’s agronomic system. In the search for improved nitrogen rate decisions, what approach is most often taken and are those approaches justified through scientific investigation? The objective of this presentation is to educate decision makers on how these algorithms are developed and evaluate how well they work in the field on a small-plot... R.W. Mullen, S.B. Phillips, W.R. Raun, W.E. Thomason Download paper
2. Pest Detection on UAV Imagery Using a Deep Convolutional Neural Network Presently, precision agriculture uses remote sensing for the mapping of crop biophysical parameters with vegetation indices in order to detect problematic areas, and then send a human specialist for a targeted field investigation. The same principle is applied for the use of UAVs in precision agriculture, but with finer spatial resolutions. Vegetation mapping with UAVs requires the mosaicking of several images, which results in significant geometric and radiometric problems. Furthermore, even... Y. Bouroubi, P. Bugnet, T. Nguyen-xuan, C. Bélec, L. Longchamps, P. Vigneault, C. Gosselin Download paper

Filter results2 paper(s) found.

1. Developing Nitrogen Algorithms for Corn Production Using Optical Sensors

Remote sensing for nitrogen management in cereal crops has been an intensive research area due to environmental concerns and economic realities of today’s agronomic system. In the search for improved nitrogen rate decisions, what approach is most often taken and are those approaches justified through scientific investigation? The objective of this presentation is to educate decision makers on how these algorithms are developed and evaluate how well they work in the field on a small-plot... R.W. Mullen, S.B. Phillips, W.R. Raun, W.E. Thomason

Download paper

2. Pest Detection on UAV Imagery Using a Deep Convolutional Neural Network

Presently, precision agriculture uses remote sensing for the mapping of crop biophysical parameters with vegetation indices in order to detect problematic areas, and then send a human specialist for a targeted field investigation. The same principle is applied for the use of UAVs in precision agriculture, but with finer spatial resolutions. Vegetation mapping with UAVs requires the mosaicking of several images, which results in significant geometric and radiometric problems. Furthermore, even... Y. Bouroubi, P. Bugnet, T. Nguyen-xuan, C. Bélec, L. Longchamps, P. Vigneault, C. Gosselin

Download paper