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Griffin, S
Karampoiki, M
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Authors
Griffin, S
Darr, M.J
Karampoiki, M
Todman, L
Mahmood, S
Murdoch, A
Paraforos, D
Hammond, J
Ranieri, E
Topics
Spatial Variability in Crop, Soil and Natural Resources
Big Data, Data Mining and Deep Learning
Type
Oral
Year
2010
2022
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1. Assessment Of The Success Of Variable Rate Seeding Based On EMI Maps

  Good plant establishment is the critical first step in growing a crop. To achieve this, the correct seed rate must be calculate. This is done by assessing the optimum target plant population per m² and then making an estimate of any  losses over winter. Losses will depend on the quality of seedbed created which is related to texture, stoniness and compaction of the soil. If there is any variation in these field characteristics then the correct seed... S. Griffin, M. Darr

2. 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