Proceedings
Authors
| Filter results3 paper(s) found. |
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1. A Bayesian Network Approach to Wheat Yield Prediction Using Topographic, Soil and Historical DataBayesian 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 |
2. Finnish Future Farm Speeding Up the Uptake of Precision AgricultureThe Finnish Future Farm (FFF) is an innovative concept that seamlessly integrates a physical Smart Farm with a Digital Twin, complemented by educational programs and business development opportunities. This holistic approach aims to propel the evolution of Smart Agriculture in Finland. At its core, FFF is a platform for co-creation with a strong emphasis on User-Centered Design. It employs a Multi-Actor Approach, bringing together companies, experts, researchers, and end users to collaborate... H.E. Haapala |
3. Report from Finland - How We Speed Up Innovation Uptake in Agriculture in FinlandFinnish agriculture is rapidly digitalizing. While the number of farms is decreasing, those that remain are increasingly adopting new technologies. Finns have a tradition of being early adopters of mobile technologies, with the Finnish phone company Nokia being a notable forerunner. However, in agriculture, users tend to be more conservative, resulting in lower than expected adoption rates of Precision Farming. The reasons for this are not only financial but also related to the usability issues... H.E. Haapala |