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Open AccessArticle

Advanced Data Analysis as a Tool for Net Blotch Density Estimation in Spring Barley

1
Control Engineering, Environmental and Chemical Engineering Research Unit, University of Oulu, 90014 Oulu, Finland
2
Natural Resources Institute Finland, 31600 Jokioinen, Finland
*
Author to whom correspondence should be addressed.
Agriculture 2020, 10(5), 179; https://doi.org/10.3390/agriculture10050179
Received: 27 April 2020 / Revised: 12 May 2020 / Accepted: 14 May 2020 / Published: 19 May 2020
(This article belongs to the Section Crop Protection, Diseases, Pest and Weeds)
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. View Full-Text
Keywords: advanced data analysis; feature generation; plant disease prediction; modern agriculture advanced data analysis; feature generation; plant disease prediction; modern agriculture
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Ruusunen, O.; Jalli, M.; Jauhiainen, L.; Ruusunen, M.; Leiviskä, K. Advanced Data Analysis as a Tool for Net Blotch Density Estimation in Spring Barley. Agriculture 2020, 10, 179.

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