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Schulthess, U
Karampoiki, M
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Authors
Schulthess, U
Schelling, K
Rodrigues Jr., F.A
Ortiz-Monasterio, I
Zarco-Tejada, P.J
Toledo, F.H
Schulthess, U
Gérard, B
Karampoiki, M
Todman, L
Mahmood, S
Murdoch, A
Paraforos, D
Hammond, J
Ranieri, E
Topics
Precision A to Z for Practitioners
Remote Sensing Applications in Precision Agriculture
Big Data, Data Mining and Deep Learning
Type
Poster
Oral
Year
2012
2016
2022
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1. Beyond NDVI - Additional Benefits of RapidEye Image Products

... U. Schulthess, K. Schelling

2. High Resolution Hyperspectral Imagery to Assess Wheat Grain Protein in a Farmer's Field

The agricultural research sector is working to develop new technologies and management knowledge to sustainably increase food productivity, to ensure global food security and decrease poverty. Wheat is one of the most important crops into this scenario, being among the three most important cereal commodities produced worldwide. Precision Agriculture (PA) and specially Remote Sensing (RS) technologies have become in the recent years more affordable which has improved the availability and flexibility... F.A. Rodrigues jr., I. Ortiz-monasterio, P.J. Zarco-tejada, F.H. Toledo, U. Schulthess, B. Gérard

3. A Bayesian Network Approach to Wheat Yield Prediction Using Topographic, Soil and Historical Data

Bayesian Network (BN) is the most popular approach for modeling in the agricultural domain. Many successful applications have been reported for crop yield prediction, weed infestation, and crop diseases. BN uses probabilistic relationships between variables of interest and in combination with statistical techniques the data modeling has many advantages. The main advantages are that the relationships between variables can be learned using the model as well as the potential to deal with missing... M. Karampoiki, L. Todman, S. Mahmood, A. Murdoch, D. Paraforos, J. Hammond, E. Ranieri