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Social and Behavioral Sciences Commons™
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Full-Text Articles in Social and Behavioral Sciences
Harvesting Fertilized Rye Cover Crop: Simulated Revenue, Net Energy, And Drainage Nitrogen Loss, R. W. Malone, J. F. Obrycki, Douglas L. Karlen, T. C. Kaspar, D. B. Jaynes, T. B. Parkin, S. H. Lence, G. W. Feyereisen, Q. X. Fang, T. L. Richard, K. Gillette
Harvesting Fertilized Rye Cover Crop: Simulated Revenue, Net Energy, And Drainage Nitrogen Loss, R. W. Malone, J. F. Obrycki, Douglas L. Karlen, T. C. Kaspar, D. B. Jaynes, T. B. Parkin, S. H. Lence, G. W. Feyereisen, Q. X. Fang, T. L. Richard, K. Gillette
Douglas L Karlen
Harvesting fertilized rye (Secale cereale L.) cover crop has been suggested as a method to increase producer revenue and biofuel feedstock production, but drainage N loss impacts are currently unknown. Using the tested Root Zone Water Quality Model (RZWQM) across several N rates, spring application of 120 kg N ha-1 prior to winter rye harvest reduced drainage N loss by 54% compared with no cover crop and by 18% compared with planted rye that was neither fertilized nor harvested. Estimates of producer revenue and net energy were also positive, with 8.3 Mg ha-1 of harvested rye biomass. …
Crowdsourcing Image Analysis For Plant Phenomics To Generate Ground Truth Data For Machine Learning, Naihui Zhou, Zachary D. Siegel, Scott Zarecor, Nigel Lee, Darwin A. Campbell, Carson M. Andorf, Dan Nettleton, Carolyn J. Lawrence-Dill, Baskar Ganapathysubramanian, Jonathan W. Kelly, Iddo Friedberg
Crowdsourcing Image Analysis For Plant Phenomics To Generate Ground Truth Data For Machine Learning, Naihui Zhou, Zachary D. Siegel, Scott Zarecor, Nigel Lee, Darwin A. Campbell, Carson M. Andorf, Dan Nettleton, Carolyn J. Lawrence-Dill, Baskar Ganapathysubramanian, Jonathan W. Kelly, Iddo Friedberg
Dan Nettleton
The accuracy of machine learning tasks critically depends on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. Here we explore the use of crowdsourcing to generate a large number of training data of good quality. We explore an image analysis task involving the segmentation of corn tassels from images taken in a field setting. We investigate the accuracy, speed and other quality metrics when this task is performed by students for academic credit, Amazon MTurk workers, and Master Amazon MTurk …