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The performance of meteorological data-based methods to forecast plant diseases strongly depends on temporal weather information. In this paper, a data analysis procedure is presented for finding the optimal starting time for forecasting net blotch density in spring barley based on meteorological data. For this purpose, changes in the information content of typically measured weather variables were systemically quantified in sliding time windows and with additionally generated mathematical transformations, namely with features. Signal-to-noise statistics were applied in a novel way as a metric for identifying the optimal starting time instance and the most important features to successfully distinguish between two net blotch densities during springtime itself. According to the results, the information content of meteorological data used in classifying between nine years with and four years without net blotch reached its maximum in Finnish weather conditions on the 41st day from the beginning of the growing season. Specifically, utilising weather data at 41–55 days from the beginning of the growing season maximises successful forecasting potential of net blotch density. It also seems that this time instance enables a linear classification task with a selected feature subset, since the averages of the metrics in two data groups differ statistically with a minimum 68% confidence level for nine days in a 14-day time window.

A novel data analysis method for the evaluation of plant disease risk that utilizes weather information is presented in this paper. This research considers two different datasets: open weather data from the Finnish Meteorological Institute and long-term (1991–2017) plant disease severity observations in different hardiness zones in Finland. Historical net blotch severity data on spring barley were collected from official variety trials carried out by the Natural Resources Institute Finland (Luke) and the analysis was performed with existing data without additional measurements. Feature generation was used to combine different datasets and to enrich the information content of the data. The t-test was applied to validate features and select the most suitable one for the identification of datasets with high net blotch risk. Based on the analysis, the selected daily measured variables for the estimation of net blotch density were the average temperature, minimum temperature, and rainfall. The results strongly indicate that thorough data analysis and feature generation methods enable new tools for plant disease prediction. This is crucial when predicting the disease risk and optimizing the use of pesticides in modern agriculture. Here, the developed system resolves the correlation between weather measurements and net blotch observations in a novel way.