Abstract
Downy mildew, powdery mildew, and gray mold are major diseases of grapevine with a strong negative impact on fruit yield and fruit quality. These diseases are controlled by the application of chemicals, which may cause undesirable effects on the environment and on human health. Thus, monitoring and forecasting crop disease is essential to support integrated pest management (IPM) measures. In this study, two tree-based machine learning (ML) algorithms, random forest and C5.0, were compared to test their capability to predict the appearance of symptoms of grapevine diseases, considering meteorological conditions, spatial indices, the number of crop protection treatments and the frequency of monitoring days in which symptoms were recorded in the previous year. Data collected in Tuscany region (Italy), on the presence of symptoms on grapevine, from 2006 to 2017 were divided with an 80/20 proportion in training and test set, data collected in 2018 and 2019 were tested as independent years for downy mildew and powdery mildew. The frequency of symptoms in the previous year and the cumulative precipitation from April to seven days before the monitoring day were the most important variables among those considered in the analysis for predicting the occurrence of disease symptoms. The best performance in predicting the presence of symptoms of the three diseases was obtained with the algorithm C5.0 by applying (i) a technique to deal with imbalanced dataset (i.e., symptoms were detected in the minority of observations) and (ii) an optimized cut-off for predictions. The balanced accuracy achieved in the test set was 0.8 for downy mildew, 0.7 for powdery mildew and 0.9 for gray mold. The application of the models for downy mildew and powdery mildew in the two independent years (2018 and 2019) achieved a lower balanced accuracy, around 0.7 for both the diseases. Machine learning models were able to select the best predictors and to unravel the complex relationships among geographic indices, bioclimatic indices, protection treatments and the frequency of symptoms in the previous year.
Highlights
Downy mildew, powdery mildew, and gray mold are major diseases of grapevine (Vitis vinifera L.), affecting leaves and fruits and causing yield loss and quality decrease of must and wine
Powdery mildew is caused by Erisyphe necator Schwein., a polycyclic disease with two distinct phases: primary infections are caused by sexual spores and secondary infections are determined by asexual spores (Gadoury and Pearson, 1988), on all green tissues of grapevines, mainly leaves and berries (Gadoury et al, 2001; Caffi et al, 2011)
Similar results were found by Volpi et al (2020), who applied machine learning (ML) algorithms for predicting the probability of infestation by Bactrocera oleae on olive trees, founding that C5.0 had a higher Receiver Operating Characteristic (ROC) compared to k-nearest neighbors (k-NN), Classification and Regression Trees (CART), Random Forest (RF) and Neural Network (NN)
Summary
Powdery mildew, and gray mold are major diseases of grapevine (Vitis vinifera L.), affecting leaves and fruits and causing yield loss and quality decrease of must and wine. Is the causal agent of gray mold and in grapevine infects all green tissues, ripening berries, with different infection pathways for conidia (inflorescences, young clusters and ripening berries) and mycelium (berry-toberry) (Elmer et al, 2007). Because these pathogens may cause severe symptoms on grapevines at the beginning of infection, control strategies have focused on early treatments, even in integrated pest management (IPM), as prevention to stop the pathogen outbreak before its establishment. A reliable monitoring and forecasting system is essential for deriving prediction indices in support of sustainable protection measures (e.g., Marchi et al, 2016)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
