Employing weather-based disease and machine learning techniques for optimal control of Septoria Leaf Blotch and Stripe Rust in wheat

El Jarroudi, Moussa and Lahlali, Rachid and El Jarroudi, Haifa and Tychon, Bernard and Belleflamme, Alexandre and Junk, Jurgen and Denis, Antoine and El Jarroudi, Mustapha and Kouadio, Louis ORCID: https://orcid.org/0000-0001-9669-7807 (2020) Employing weather-based disease and machine learning techniques for optimal control of Septoria Leaf Blotch and Stripe Rust in wheat. In: 2nd International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD 2019), 8-11 July 2019, Cham, Morocco.

[img]
Preview
Text (Published version)
Published_version.pdf
Available under License Creative Commons Attribution 4.0.

Download (765kB) | Preview

Abstract

Septoria tritici blotch (STB) is among the most important crop diseases causing continuous threats to wheat production worldwide. STB epidemics are the outcome of interactions between susceptible host cultivars, favorable environmental conditions, and sufficient quantities of pathogen inoculum. Thus, to determine whether fungicide sprays should be applied to prevent the risk of epidemics that might otherwise lead to yield loss, weather-based systems as stand-alone or combined with other disease or agronomic variables have been implemented in decision-support systems (DSS). Given the economic importance of wheat in Morocco and increasing concerns caused by fungal plant pathogens in wheat-growing regions, DSS integrating a disease risk model would help to limit potentially harmful side effects of fungicide applications while ensuring economic benefits. Here we describe the use of an artificial intelligence algorithm, i.e. the artificial neural network, within a weather-based modelling approach to predict the progress of STB in wheat in Luxembourg. The reproducibility of area-specific modelling approaches is often a hurdle for their application in operational disease warning system at a regional scale. Hence, we explore the potential of coupling artificial intelligence algorithms with weather-based model for predicting in-season progress of a major economically important fungal disease – wheat stripe rust – in selected wheat-producing regions in Morocco.


Statistics for USQ ePrint 42209
Statistics for this ePrint Item
Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Paper made available according to Creative Commons: Attribution 4.0 license.
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Applied Climate Sciences (1 Aug 2018 -)
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Applied Climate Sciences (1 Aug 2018 -)
Date Deposited: 16 Jun 2021 23:51
Last Modified: 30 Jun 2021 22:18
Uncontrolled Keywords: Septoria Leaf Blotch; Stripe Rust; wheat
Fields of Research (2008): 07 Agricultural and Veterinary Sciences > 0703 Crop and Pasture Production > 070308 Crop and Pasture Protection (Pests, Diseases and Weeds)
Fields of Research (2020): 30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3004 Crop and pasture production > 300409 Crop and pasture protection (incl. pests, diseases and weeds)
Identification Number or DOI: https://doi.org/10.1007/978-3-030-36664-3_18
URI: http://eprints.usq.edu.au/id/eprint/42209

Actions (login required)

View Item Archive Repository Staff Only