Proceedings :: International Society of Precision Agriculture

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Jacquin, A

Samborski, S.M

Ritchie, G

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Authors

Jacquin, A

Sigel, G

Hagolle, O

Lepoivre, B

Roumiguié, A

Poilvé, H

Karn, R

Adedeji, O

Ghimire, B.P

Abdalla, A

Sheng, V

Ritchie, G

Guo, W

Samborski, S.M

Torres, U

Leszczyńska, R

Bech, A

Bagavathiannan, M

Ghimire, B

Karn, R

Adedeji, O

Ritchie, G

Guo, W

Topics

Sensor Application in Managing In-season CropVariability

Precision Agriculture and Global Food Security

Scouting and Field Data collection with Unmanned Aerial Systems

Decision Support Systems

Type

Oral

Year

2014

2024

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Filter results4 paper(s) found.
1. 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
2. Within Field Cotton Yield Prediction Using Temporal Satellite Imagery Combined with Deep Learning Crop yield prediction at the field scale plays a pivotal role in enhancing agricultural management, a vital component in addressing global food security challenges. Regional or county-level data, while valuable for broader agricultural planning, often lacks the precision required by farmers for effective and timely field management. The primary obstacle in utilizing satellite imagery to forecast crop yields at the field level lies in its low temporal and spatial resolutions. This study aims to... R. Karn, O. Adedeji, B.P. Ghimire, A. Abdalla, V. Sheng, G. Ritchie, W. Guo
3. The Relationship Between Vegetation Indices Derived from UAV Imagery and Maturity Class in Potato Breeding Trials In potato breeding, maturity class (MC) is a crucial selection criterion because this is a critical aspect of commercial potato production. Currently, the classification of potato genotypes into MCs is done visually, which is time- and labor-consuming. Unmanned aerial vehicles (UAVs) equipped with sensors can acquire images with high spatial and temporal resolution. The objectives of this study were to 1) establish the relationship between vegetation indices (VIs) derived from UAV imagery at three... S.M. Samborski, U. Torres, R. Leszczyńska, A. Bech, M. Bagavathiannan Download paper
4. Simulating Climate Change Impacts on Cotton Yield in the Texas High Plains Crop yield prediction enables stakeholders to plan farming practices and marketing. Crop models can predict crop yield based on cropping system and practices, soil, and other environmental factors. These models are being used for decision support in agriculture in a variety of ways. Cultivar selection, water and nutrient input optimization, planting date selection, climate change analysis and yield prediction are some of the promising area of applications of the models in field level farm management.... B. Ghimire, R. Karn, O. Adedeji, G. Ritchie, W. Guo

Filter results4 paper(s) found.

1. 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

2. Within Field Cotton Yield Prediction Using Temporal Satellite Imagery Combined with Deep Learning

Crop yield prediction at the field scale plays a pivotal role in enhancing agricultural management, a vital component in addressing global food security challenges. Regional or county-level data, while valuable for broader agricultural planning, often lacks the precision required by farmers for effective and timely field management. The primary obstacle in utilizing satellite imagery to forecast crop yields at the field level lies in its low temporal and spatial resolutions. This study aims to... R. Karn, O. Adedeji, B.P. Ghimire, A. Abdalla, V. Sheng, G. Ritchie, W. Guo

3. The Relationship Between Vegetation Indices Derived from UAV Imagery and Maturity Class in Potato Breeding Trials

In potato breeding, maturity class (MC) is a crucial selection criterion because this is a critical aspect of commercial potato production. Currently, the classification of potato genotypes into MCs is done visually, which is time- and labor-consuming. Unmanned aerial vehicles (UAVs) equipped with sensors can acquire images with high spatial and temporal resolution. The objectives of this study were to 1) establish the relationship between vegetation indices (VIs) derived from UAV imagery at three... S.M. Samborski, U. Torres, R. Leszczyńska, A. Bech, M. Bagavathiannan

Download paper

4. Simulating Climate Change Impacts on Cotton Yield in the Texas High Plains

Crop yield prediction enables stakeholders to plan farming practices and marketing. Crop models can predict crop yield based on cropping system and practices, soil, and other environmental factors. These models are being used for decision support in agriculture in a variety of ways. Cultivar selection, water and nutrient input optimization, planting date selection, climate change analysis and yield prediction are some of the promising area of applications of the models in field level farm management.... B. Ghimire, R. Karn, O. Adedeji, G. Ritchie, W. Guo