Proceedings
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| Filter results2 paper(s) found. |
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1. Challenges and Successes when Generating In-season Multi-temporal Calibrated Aerial ImageryDigital aerial imagery (DAI) of the crop canopy collected by aircraft and unmanned aerial vehicles is the yardstick of precision agriculture. However, the quantitative use of this imagery is often limited by its variable characteristics, low quality, and lack of radiometric calibration. To increase the quality and utility of using DAI in crop management, it is important to evaluate and address these limitations of DAI. Even though there have been improvements in spatial resolution... P.M. Kyveryga, J. Pritsolas, J. Connor, R. Pearson |
2. 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 |