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Whelan, B.M
Kruse, R
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Authors
Ru, G
Schneider, M
Kruse, R
Filippi, P
Jones, E.J
Fajardo, M
Whelan, B.M
Bishop, T.F
Topics
Spatial Variability in Crop, Soil and Natural Resources
Big Data, Data Mining and Deep Learning
Type
Oral
Year
2010
2018
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1. A Clustering Approach For Management Zone Delineation In Precision Agriculture

In recent years, an increasing amount of research has been devoted to the delineation of management zones. There have been quite a number of approaches towards using small-scale data for subdividing the field into a small number of zones, usually three or four. However, these zones are usually static, often require multi-year data sets and are based on low-resolution sampling methods for data acquisition. Furthermore, existing research into the... G. Ru, M. Schneider, R. Kruse

2. Forecasting Crop Yield Using Multi-Layered, Whole-Farm Data Sets and Machine Learning

The ultimate goal of Precision Agriculture is to improve decision making in the business of farming. Many broadacre farmers now have a number of years of crop yield data for their fields which are often augmented with additional spatial data, such as apparent soil electrical conductivity (ECa), soil gamma radiometrics, terrain attributes and soil sample information. In addition there are now freely available public datasets, such as rainfall, digital soil maps and archives of satellite remote... P. Filippi, E.J. Jones, M. Fajardo, B.M. Whelan, T.F. Bishop