Proceedings
Authors
| Filter results5 paper(s) found. |
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1. Changes Of Data Sampling Procedure To Avoid Energy And Data Losses During Microclimates Monitoring With Wireless Sensor Networks... J.C. Benavente, C.E. Cugnasca, M.F. Barros, H.P. Santos, G. Http://icons.paqinteractive.com/16x16/ac |
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. Can Soil Fertility Data and Topography Predict Yield Stability Zones for Corn Fields in New York?Yield monitor systems play a vital role in precision agriculture given their ability to capture and map within-field yield variability. When three or more years of yield data are available, yield stability zone maps can be generated to show both the spatial and temporal variability of yield within a field. Based on the farm’s overall temporal mean and standard deviation for a specific crop, we can classify areas in the field as consistently high- (Q1) or low-yielding (Q4), and variably high-... M. Marcaida, X. Zhang, S. Srinivasagan, S. Shajahan, Q. Ketterings |
4. Cyberinfrastructure for Machine Learning Applications in Agriculture: Experiences, Analysis, and VisionAdvancements in machine learning algorithms and GPU computational speeds over the last decade have led to remarkable progress in the capabilities of machine learning. This progress has been so much that, in many domains, including agriculture, access to sufficiently diverse and high-quality datasets has become a limiting factor. While many agricultural use cases appear feasible with current compute resources and machine learning algorithms, the lack of software infrastructure for collecting,... L. Waltz, S. Khanal, S. Katari, C. Hong, A. Anup, J. Colbert, A. Potlapally, T. Dill, C. Porter, J. Engle, C. Stewart, H. Subramoni, R. Machiraju, O. Ortez, L. Lindsey, A. Nandi |
5. Single-strip Spatial Evaluation Approach: a Simplified Method for Enhanced Sustainable Farm ManagementOn-farm experimentation (OFE) plays a pivotal role in evaluating and validating the effectiveness of agricultural practices and products. The results of OFE enable farmers to act and make changes that can enhance the farm’s economic and environmental sustainability. Experimental designs can be a barrier to the adoption of OFE. The conventional approach often involves randomized complete block designs with 3 to 5 replications in the field, which can be space-intensive and disrupt workflow... S. Srinivasagan, Q. Ketterings, M. Marcaida, S. Shajahan, J. Ramos-tanchez, J. Cho, , L. Thompson, J. Guinness, R. Goel |