Modeling Agricultural Production Activities Using Weather and Soil Parameters

摘要

Activities concerning agricultural production processes are highly influenced by weather and soil parameters. Such parameters determine when certain activities in the agricultural process should be carried out. For example, it is known that fertilization should be applied when humidity of the soil is appropriate for fertilizer to get worked into it, as too dry soil causes fertilizer to be washed away. Furthermore, pest and disease control is mostly affected by wind direction and speed, which may cause pesticide to be overly spread or concentrated in some spot on the field. There is well known expert knowledge about connection between those parameters and scheduling production process activities. However, expert knowledge can be enriched and automatized with data-driven models, which are relatively unknown due to lack of research in this field. We propose a Bayesian logistic regression model in order to predict probability of conducting field activities, based on weather and soil parameters. We have trained and validated our model on historical data which contains activities during potato production in Croatia and weather station measurements gathered from several fields over a period of one season.