Decision Trees to Forecast Risks of Strawberry Powdery Mildew Caused by Podosphaera aphanis

Odile Carisse and Mamadou Lamine Fall
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Odile Carisse: Saint-Jean-sur-Richelieu Research and Development Centre, Agriculture and Agri-Food Canada, Saint-Jean-sur-Richelieu, QC J3B 3E6, Canada
Mamadou Lamine Fall: Saint-Jean-sur-Richelieu Research and Development Centre, Agriculture and Agri-Food Canada, Saint-Jean-sur-Richelieu, QC J3B 3E6, Canada

Agriculture, 2021, vol. 11, issue 1, 1-16

Abstract: Powdery mildew ( Podosphaera aphanis ) is a major disease in day-neutral strawberry. Up to 30% yield losses have been observed in Eastern Canada. Currently, management of powdery mildew is mostly based on fungicide applications without consideration of risk. The objective of this study is to use P . aphanis inoculum, host ontogenic resistance, and weather predictors to forecast the risk of strawberry powdery mildew using CART models (classification trees). The data used to build the trees were collected in 2006, 2007, and 2008 at one experimental farm and six commercial farms located in two main strawberry-production areas, while external validation data were collected at the same experimental farm in 2015, 2016, and 2018. Data on proportion of leaf area diseased (PLAD) were grouped into four severity classes (1: PLAD = 0; 2: PLAD > 0 and <5%; 3: >5% and <15%; and 4: PLAD > 15%) for a total of 681 and 136 cases for training and external validation, respectively. From the initial 92 weather variables, 21 were selected following clustering. The tree with the best balance between the number of predictors and highest accuracy was built with: airborne inoculum concentration and number of susceptible leaves on the day of sampling, and mean relative humidity, mean daily number of hours at temperature between 18 and 30 °C, and mean daily number of hours at saturation vapor pressure between 10 and 25 mmHg during the previous 6 days. For training, internal validation, and external validation datasets, the sensitivity, specificity, and accuracy ranged from 0.70 to 0.90, 0.87 to 0.98, and 0.82 to 0.97, respectively. The classification rules to estimate strawberry powdery mildew risk can be easily implemented into disease decision support systems and used to treat only when necessary and thus avoid preventable yield losses and unnecessary treatments.

Keywords: data mining; disease decision support system; disease risk; epidemiology; machine learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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