Proceedings
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| Filter results4 paper(s) found. |
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1. Revising Nitrogen Recommendations For Wheat In Response To The Need For Support Of Variable-rate Nitrogen ApplicationSampling studies in North Dakota conducted from 1994 to 2003 showed that variable-rate N application could be practically directed with zone soil sampling. Results from variable-rate N studies using zone soil sampling were often less than rewarding due in part to the use of a whole-field predicted yield-based formula for developing the N recommendation in each zone. Nitrogen rate studies on spring wheat and durum were established in 2005 through 2009 to reexamine N recommendations. The results... D. Franzen, G. Endres, R. Ashley, J. Staricka, J. Lukach, K. Mckay |
2. Spatial Decision Support System: Controlled Tile Drainage – Calculate Your BenefitsClimate projection studies suggest that extreme heat waves and floods will become more frequent, affecting future crop yields by 20%-30%, globally. Managing vulnerability and risk begins at the farm level where best management practices can reduce the impacts associated with extreme weather events. A practice that can assist in mitigating the impact of some extreme events is controlled tile drainage (CTD). With CTD, producers use water flow control structures to manage the drainage of water from... A. Kross, G. Kaur, D. Callegari, D. Lapen, M. Sunohara, H. Mcnairn, H. Rudy, L. Van vliet |
3. Evaluation of an Artificial Neural Network Approach for Prediction of Corn and Soybean YieldThe ability to predict crop yield during the growing season is important for crop income, insurance projections and for evaluating food security. Yet, modeling crop yield is challenging because of the complexity of the relationships between crop growth and the interrelated predictor variables. Artificial neural networks (ANNs) are useful for such complex systems as they can capture non-linear relationships of data without explicitly knowing the underlying processes. In this study, an ANN-based... A. Kross, G. Kaur, E. Znoj, D. Callegari, M. Sunohara, H. Mcnairn, D. Lapen, H. Rudy, L. Van vliet |
4. Supervised Feature Selection and Clustering for Equine Activity RecognitionIn this paper we introduce a novel supervised algorithm for equine activity recognition based on accelerometer data. By combining an approach of calculating a wide variety of time-series features with a supervised feature significance test we can obtain the best suited features using just 5 labeled samples per class and without requiring any expert domain knowledge. By using a simple cluster assignment algorithm with these obtained features, we get a classification algorithm that achieves a mean... T. De waele, D. Peralta, A. Shahid, E. De poorter |