University of Oulu

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.

Advanced data analysis as a tool for net blotch density estimation in spring barley

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Author: Ruusunen, Outi1; Jalli, Marja2; Jauhiainen, Lauri2;
Organizations: 1Control Engineering, Environmental and Chemical Engineering Research Unit, University of Oulu, 90014 Oulu, Finland
2Natural Resources Institute Finland, 31600 Jokioinen, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.6 MB)
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Language: English
Published: Multidisciplinary Digital Publishing Institute, 2020
Publish Date: 2020-06-23


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.

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Series: Agriculture
ISSN: 2077-0472
ISSN-E: 2077-0472
ISSN-L: 2077-0472
Volume: 10
Issue: 5
Article number: 179
DOI: 10.3390/agriculture10050179
Type of Publication: A1 Journal article – refereed
Field of Science: 218 Environmental engineering
4111 Agronomy
Funding: This research was funded by the Ministry of Agriculture and Forestry of Finland, Document number 632/03.01.02/2017.
Copyright information: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (