Proceedings :: International Society of Precision Agriculture

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Charvat Jr., K

Murdoch, A

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Charvat, K

Reznik, T

Lukas, V

Charvat Jr., K

Horakova, S

Splichal, M

Kepka, M

Charvat, K

Berzins, R

Bergheim, R

Zadrazil, F

Macura, J

Langovskis, D

Snevajs, H

Kubickova, H

Horakova, S

Charvat Jr., K

Karampoiki, M

Todman, L

Mahmood, S

Murdoch, A

Paraforos, D

Hammond, J

Ranieri, E

Topics

Precision Agriculture and Climate Change

Geospatial Data

Big Data, Data Mining and Deep Learning

Type

Oral

Poster

Year

2016

2022

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Filter results3 paper(s) found.
1. Quo Vadis Precision Farming The agriculture sector is a unique sector due to its strategic importance for both citizens and economy which, ideally, should make the whole sector a network of interacting organizations. There is an increasing tension, the like of which is not experienced in any other sector, between the requirements to assure full safety and keep costs under control, but also assure the long-term strategic interests of Europe and worldwide. In that sense, agricultural production influences, and is influenced... K. Charvat, T. Reznik, V. Lukas, K. Charvat jr., S. Horakova, M. Splichal, M. Kepka Download paper
2. Map Whiteboard As Collaboration Tool for Smart Farming Advisory Services Precision agriculture, a branch of smart farming, holds great promise for modernization of European agriculture both in terms of environmental sustainability and economic outlook. The vast data archives made available through Copernicus and related infrastructures, combined with a low entry threshold into the domain of AI-technologies has made it possible, if not outright easy, to make meaningful predictions that divides individual agricultural fields into zones where variable rates... K. Charvat, R. Berzins, R. Bergheim, F. Zadrazil, J. Macura, D. Langovskis, H. Snevajs, H. Kubickova, S. Horakova, K. Charvat jr. Download paper
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 Download paper

Filter results3 paper(s) found.

1. Quo Vadis Precision Farming

The agriculture sector is a unique sector due to its strategic importance for both citizens and economy which, ideally, should make the whole sector a network of interacting organizations. There is an increasing tension, the like of which is not experienced in any other sector, between the requirements to assure full safety and keep costs under control, but also assure the long-term strategic interests of Europe and worldwide. In that sense, agricultural production influences, and is influenced... K. Charvat, T. Reznik, V. Lukas, K. Charvat jr., S. Horakova, M. Splichal, M. Kepka

Download paper

2. Map Whiteboard As Collaboration Tool for Smart Farming Advisory Services

Precision agriculture, a branch of smart farming, holds great promise for modernization of European agriculture both in terms of environmental sustainability and economic outlook. The vast data archives made available through Copernicus and related infrastructures, combined with a low entry threshold into the domain of AI-technologies has made it possible, if not outright easy, to make meaningful predictions that divides individual agricultural fields into zones where variable rates... K. Charvat, R. Berzins, R. Bergheim, F. Zadrazil, J. Macura, D. Langovskis, H. Snevajs, H. Kubickova, S. Horakova, K. Charvat jr.

Download paper

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

Download paper