Proceedings :: International Society of Precision Agriculture

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Hammond, J

Hunt, A

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Authors

Nerpel, D

Ellsworth, J.W

Hunt, A

Karampoiki, M

Todman, L

Mahmood, S

Murdoch, A

Paraforos, D

Hammond, J

Ranieri, E

Topics

Standards & Data Stewardship

Big Data, Data Mining and Deep Learning

Type

Oral

Year

2016

2022

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Filter results2 paper(s) found.
1. Modus: a Standard for Big Data Modus Standard is a system of defined terminology, agreed metadata and file transfer format that has grown from a need to exchange, merge and trend agricultural testing data. The three presenters will discuss steps taken to develop the system, benefits to data exchange, current user base and additions being made to the standard. ... D. Nerpel, J.W. Ellsworth, A. Hunt Download paper
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 Download paper

Filter results2 paper(s) found.

1. Modus: a Standard for Big Data

Modus Standard is a system of defined terminology, agreed metadata and file transfer format that has grown from a need to exchange, merge and trend agricultural testing data. The three presenters will discuss steps taken to develop the system, benefits to data exchange, current user base and additions being made to the standard. ... D. Nerpel, J.W. Ellsworth, A. Hunt

Download paper

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

Download paper