Proceedings :: International Society of Precision Agriculture

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Stevens, L.J

Sheng, V

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Stevens, L.J

Ferguson, R.B

Franzen, D.W

Kitchen, N.R

Karn, R

Adedeji, O

Ghimire, B.P

Abdalla, A

Sheng, V

Ritchie, G

Guo, W

Topics

Sensor Application in Managing In-season CropVariability

Precision Agriculture and Global Food Security

Type

Oral

Year

2014

2024

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Filter results2 paper(s) found.
1. In-Season Nitrogen Requirement For Maize Using Model And Sensor-Based Recommendation Approaches Nitrogen (N), an essential element, is often limiting to plant growth. There is great value in determining the optimum quantity and timing of N application to meet crop needs while minimizing losses. Low nitrogen use efficiency (NUE) has been attributed to several factors including poor synchrony between N fertilizer and crop demand, unaccounted for spatial variability resulting in varying crop N needs, and temporal variances in crop N needs. Applying a portion... L.J. Stevens, R.B. Ferguson, D.W. Franzen, N.R. Kitchen 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

Filter results2 paper(s) found.

1. In-Season Nitrogen Requirement For Maize Using Model And Sensor-Based Recommendation Approaches

Nitrogen (N), an essential element, is often limiting to plant growth. There is great value in determining the optimum quantity and timing of N application to meet crop needs while minimizing losses. Low nitrogen use efficiency (NUE) has been attributed to several factors including poor synchrony between N fertilizer and crop demand, unaccounted for spatial variability resulting in varying crop N needs, and temporal variances in crop N needs. Applying a portion... L.J. Stevens, R.B. Ferguson, D.W. Franzen, N.R. Kitchen

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