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Sadeque, Z
Jiang, H
Stevens, L.J
Spina, A.N
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Authors
Stevens, L.J
Ferguson, R.B
Franzen, D.W
Kitchen, N.R
Casiano, P.M
Morley, T.G
Sadeque, Z
Li, D
Jiang, H
Chen, S
Wang, C
Spina, A.N
Fulton, J.P
Shearer, S.A
Berger-Wolf, T
Drewry, D
Topics
Sensor Application in Managing In-season CropVariability
Engineering Technologies and Advances
In-Season Nitrogen Management
Artificial Intelligence (AI) in Agriculture
Type
Oral
Poster
Year
2014
2018
2024
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Filter results4 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

2. GNSS Positioning Techniques For Agriculture

Broadacre, row crop and high value crops each have different positioning needs.  Within these agricultural groups, individual practices such as mapping, guidance and machine control for tillage, application and harvest each have their own Global Navigation Satellite Systems (GNSS) needs for an optimal price/performance and value equation.  New research and algorithm development by NovAtel has resulted in a significant simplification of positioning methodology with increased... P.M. Casiano, T.G. Morley, Z. Sadeque

3. 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

4. Determining Desirable Swine Traits that Correlate to High Carcass Grades for Artificial Intelligence Predictions

With the global population continuing to grow, there has been an increased stress applied to the agriculture industry to improve efficiency and yield. To achieve this goal within the cattle industry, selection and reproductive decisions have been lucrative aspects, both genetically and fiscally. Breeding animal selection impacts farms through passing on favorable market, reproductive, and temperament traits. The cattle industry has experienced genetic advancement due to the flexibility of artificial... A.N. Spina, J.P. Fulton, S.A. Shearer, T. Berger-wolf, D. Drewry