Proceedings :: International Society of Precision Agriculture

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Randhawa , R

Jiang, H

Nelson, K.J

Jacquin, A

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Authors

Randhawa , R

Jacquin, A

Sigel, G

Hagolle, O

Lepoivre, B

Roumiguié, A

Poilvé, H

Danford, D.D

Nelson, K.J

Rhea, S.T

Stelford, M.W

Ferreyra, R

Wilson, J.A

Craker, B.E

Li, D

Jiang, H

Chen, S

Wang, C

Topics

Remote Sensing Applications in Precision Agriculture

Sensor Application in Managing In-season CropVariability

Big Data, Data Mining and Deep Learning

In-Season Nitrogen Management

Type

Poster

Oral

Year

2012

2014

2018

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Filter results4 paper(s) found.
1. Spectral Characterization to Discriminate Grass Weeds from Wheat Crop Using Remote Sensing and GIS for Precision Agriculture and Environmental Sustainability Kaur, Ramanjit, Mahey RK, Mahal JS, Kingra PK and Kaur Pukhraj ... R. Randhawa Download paper
2. Development Of An Index-Based Insurance Product: Validation Of A Forage Production Index Derived From Medium Spatial Resolution fCover Time Series An index-based insurance solution is developed by Pacifica Crédit Agricole Assurances and Astrium GEO-Information to estimate and monitor the near real-time forage production in France. In this system, payouts are indexed on an indicator, called Forage Production Index (FPI), calculated using a biophysical characterization of the grassland from medium spatial resolution remote sensing time series. We used the Fraction of green Vegetation Cover (fCover) integral as... A. Jacquin, G. Sigel, O. Hagolle, B. Lepoivre, A. Roumiguié, H. Poilvé Download paper
3. ADAPT: A Rosetta Stone for Agricultural Data Modern farming requires increasing amounts of data exchange among hardware and software systems. Precision agriculture technologies were meant to enable growers to have information at their fingertips to keep accurate farm records (and calculate production costs), improve decision-making and promote efficiencies in crop management, enable greater traceability, and so forth. The attainment of these goals has been limited by the plethora of proprietary, incompatible data formats among... D.D. Danford, K.J. Nelson, S.T. Rhea, M.W. Stelford, R. Ferreyra, J.A. Wilson, B.E. Craker Download paper
4. Estimating Litchi Canopy Nitrogen Content Using Simulated Multispectral Remote Sensing Data This study aims at evaluating the performance of seven highly spatial resolution remote sensing data in litchi canopy nitrogen content estimation. The litchi canopy reflectance were collected by ASD field spectrometer. Then the canopy spectral data were resampled based on the spectral response functions of each satellite sensors (Geo-eye, GF-WFV1, Rapid-eye, WV-2, Landsat 8, WV-3, and Sentinel-2). The spectral indices in literature were derived based on the simulated data. Meanwhile, the successive... D. Li, H. Jiang, S. Chen, C. Wang Download paper

Filter results4 paper(s) found.

1. Spectral Characterization to Discriminate Grass Weeds from Wheat Crop Using Remote Sensing and GIS for Precision Agriculture and Environmental Sustainability

Kaur, Ramanjit, Mahey RK, Mahal JS, Kingra PK and Kaur Pukhraj ... R. Randhawa

Download paper

2. Development Of An Index-Based Insurance Product: Validation Of A Forage Production Index Derived From Medium Spatial Resolution fCover Time Series

An index-based insurance solution is developed by Pacifica Crédit Agricole Assurances and Astrium GEO-Information to estimate and monitor the near real-time forage production in France. In this system, payouts are indexed on an indicator, called Forage Production Index (FPI), calculated using a biophysical characterization of the grassland from medium spatial resolution remote sensing time series. We used the Fraction of green Vegetation Cover (fCover) integral as... A. Jacquin, G. Sigel, O. Hagolle, B. Lepoivre, A. Roumiguié, H. Poilvé

Download paper

3. ADAPT: A Rosetta Stone for Agricultural Data

Modern farming requires increasing amounts of data exchange among hardware and software systems. Precision agriculture technologies were meant to enable growers to have information at their fingertips to keep accurate farm records (and calculate production costs), improve decision-making and promote efficiencies in crop management, enable greater traceability, and so forth. The attainment of these goals has been limited by the plethora of proprietary, incompatible data formats among... D.D. Danford, K.J. Nelson, S.T. Rhea, M.W. Stelford, R. Ferreyra, J.A. Wilson, B.E. Craker

Download paper

4. Estimating Litchi Canopy Nitrogen Content Using Simulated Multispectral Remote Sensing Data

This study aims at evaluating the performance of seven highly spatial resolution remote sensing data in litchi canopy nitrogen content estimation. The litchi canopy reflectance were collected by ASD field spectrometer. Then the canopy spectral data were resampled based on the spectral response functions of each satellite sensors (Geo-eye, GF-WFV1, Rapid-eye, WV-2, Landsat 8, WV-3, and Sentinel-2). The spectral indices in literature were derived based on the simulated data. Meanwhile, the successive... D. Li, H. Jiang, S. Chen, C. Wang

Download paper