Proceedings
Authors
| Filter results5 paper(s) found. |
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1. Using Multiplex® to Manage Nitrogen Variability in Champagne Vineyard... L. Marine, M. Manon, G. Claire, P. Laurent, F. Mostafa, C. Zoran, B. Naima, D. Sébastien, G. Olivier |
2. In-Season Nitrogen Requirement For Maize Using Model And Sensor-Based Recommendation ApproachesNitrogen (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 |
3. Evaluating a Satellite Remote Sensing and Calibration Strip-based Precision Nitrogen Management Strategy for Corn in Minnesota and IndianaPrecision nitrogen (N) management (PNM) aims to match N supply with crop N demand in both space and time and has the potential to improve N use efficiency (NUE), increase farmer profitability, and reduce N losses and negative environmental impacts. However, current PNM adoption rate is still quite low. A remote sensing and calibration strip-based PNM strategy (RS-CS-PNM) has been developed by the Precision Agriculture Center at the University of Minnesota.... K. Mizuta, Y. Miao, A.C. Morales, L.N. Lacerda, D. Cammarano, R.L. Nielsen, R. Gunzenhauser, K. Kuehner, S. Wakahara, J.A. Coulter, D.J. Mulla, D. . Quinn, B. Mcartor |
4. Identifying Key Factors Influencing Yield Spatial Pattern and Temporal Stability for Management Zone DelineationManagement zone delineation is a practical strategy for site-specific management. Numerous approaches have been used to identify these homogenous areas in the field, including approaches using multiple years of historical yield maps. However, there are still knowledge gaps in identifying variables influencing spatial and temporal variability of crop yield that should be used for management zone delineation. The objective of this study is to identify key soil and landscape properties affecting... L.N. Lacerda, Y. Miao, K. Mizuta, K. Stueve |
5. Agrosense: AI-enabled Sensing for Precision Management of Tree CropsMonitoring the tree inventory and canopy density and height frequently is critical for researchers and farm managers. However, it is very expensive and challenging to manually complete these tasks weekly. Therefore, a low-cost and artificial intelligence (AI) enhanced sensing system, Agrosense, was developed for tree inventory, canopy height measurement, and tree canopy density classification in this study. The sensing system mainly consisted of four RGB-D cameras, two Jetson Xavier NX, and one... C. Zhou, Y. Ampatzidis, H. Guan, W. Liu, A. De oliveira costa neto, S. Kunwar, O. Batuman |