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Shcherbatyuk, N
Bishop, T.F
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Authors
Filippi, P
Jones, E.J
Fajardo, M
Whelan, B.M
Bishop, T.F
Kang, C
Karkee, M
Zhang, Q
Shcherbatyuk, N
Davadant, P
Keller, M
Topics
Big Data, Data Mining and Deep Learning
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Type
Oral
Year
2018
2022
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Filter results2 paper(s) found.

1. 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

2. Diagnosis of Grapevine Nutrient Content Using Proximal Hyperspectral Imaging

Nutrient deficiencies on grapevines could affect the fruit yield and quality, which is a major concern in vineyards. Nutrient deficiencies may be recognizable by foliar symptoms that vary by mineral nutrient and stress severity, but it is too late to manage when visible deficiency symptoms become apparent. The nutrient analysis in the laboratory is the way to get an accurate result, but it is time and cost-intensive. The differences in leaf nutrient levels also alter spectral characteristics outside... C. Kang, M. Karkee, Q. Zhang, N. Shcherbatyuk, P. Davadant, M. Keller