Predicting Daily Aerobiological Risk Level of Potato Late Blight Using C5.0 and Random Forest Algorithms under Field Conditions

Abstract

Late blight, caused by Phytophthora infestans, is a major disease of the potato crop with a strong negative impact on tuber yield and tuber quality. The control of late blight in conventional potato production systems is often through weekly application of prophylactic fungicides, moving away from a sustainable production system. In support of integrated pest management practices, machine learning algorithms were proposed as tools to forecast aerobiological risk level (ARL) of Phytophthora infestans (>10 sporangia/m3) as inoculum to new infections. For this, meteorological and aerobiological data were monitored during five potato crop seasons in Galicia (northwest Spain). Mild temperatures (T) and high relative humidity (RH) were predominant during the foliar development (FD), coinciding with higher presence of sporangia in this phenological stage. The infection pressure (IP), wind, escape or leaf wetness (LW) of the same day also were significantly correlated with sporangia according to Spearman’s correlation test. ML algorithms such as random forest (RF) and C5.0 decision tree (C5.0) were successfully used to predict daily sporangia levels, with an accuracy of the models of 87% and 85%, respectively. Currently, existing late blight forecasting systems assume a constant presence of critical inoculum. Therefore, ML algorithms offer the possibility of predicting critical levels of Phytophthora infestans concentration. The inclusion of this type of information in forecasting systems would increase the exactitude in the estimation of the sporangia of this potato pathogen.

Improved random forest classification model combined with C5.0 algorithm for vegetation feature analysis in non-agricultural environments

“…Dobrinić et al 13 employed a random forest variable selection method with reduced precision to identify the most relevant features for vegetation mapping, resulting in improved classification performance suitable for large-scale land cover classification. Meno et al 14 utilized machine learning algorithms such as random forest and C5.0 decision trees to successfully predict daily late blight spore levels, with the C5.0-optimized random forest model achieving higher accuracy. Guo et al 15 investigated the generation of regional landslide susceptibility maps using machine learning methods based on the C5.0 decision tree model and K-means clustering algorithm.…”

Opportunity of the NEGFRY Decision Support System for the Sustainable Control of Potato Late Blight in A Limia (NW of Spain)

“…A list of these DSSs per European country can be found in the Euroblight [25]. However, Spain is not on the Euroblight list because so far it does not have a proven DSS for late blight control despite the fact that the pathogen has been previously studied and monitored [20,[26][27][28][29].…”