Background: Rice is the second most important cereal crop worldwide, and the first in terms of number of people who depend on it as a major staple food. Rice blast disease is the most important biotic constraint of rice cultivation causing each year millions of dollars of losses. Despite the efforts for breeding new resistant varieties, agricultural practices and chemical control are still the most important methods for disease management. Thus, rice blast forecasting is a primary tool to support rice growers in controlling the disease. In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and WARM) and two approaches based on machine learning algorithms (M5Rules and RNN), the former inducing a rule-based model and the latter building a neural network. In situ telemetry is important to obtain quality in-field data for predictive models and this was a key aspect of the RICE-GUARD project on which this study is based. According to the authors, this is the first time process-based and machine learning modelling approaches for supporting plant disease management are compared. Results: Results clearly showed that the models succeeded in providing a warning of rice blast onset and presence, thus representing suitable solutions for preventive remedial actions targeting the mitigation of yield losses and the reduction of fungicide use. Statistical metrics were derived to quantify the ability of the models to anticipate rice blast onset and detect its presence. For this, the real blast severity was used as the output for training and testing the models. The Area Under Curve (AUC) was used as a metric to quantify the "early warning" success during the early period of the crop growing season. The M5Rules, RNN and Yoshino models all gave significant "signals" during the "early warning" period (15 th /20 th June to 7 th July), with average AUC values of 214, 181 and 163, respectively. WARM also gave some signals during this period but of a spiky form so the AUC value of 39 was relatively lower for this reason. The best average values of %MAE, R and R 2 , for the machine learning models were 0.78, 0.61 and 0.72, respectively and for the process models the corresponding values were 0.65, 0.46 and 0.36. This has relevant implications for the operational use of the models, since most of the available studies limited to analyse the relationship between model outputs and the incidence of rice blast. Results also showed that machine learning methods approximated the performances of two process-based models used for years in operational contexts. Process-based and data-driven models can be used to provide early warnings to anticipate rice blast and detect its presence, thus supporting fungicide applications. Data-driven models derived from machine learning methods are a viable alternative to process-based approaches and -in cases when training datasets are available -offer a potentially greater adaptability to new contexts.
David et al. REF compared four models, two rule-based models and two neural based models for disease classification of plant leaves .
Predicting rice blast disease: machine learning versus process based models
BMC Bioinformatics volume 20, Article number: 514 (2019)
Biology Computer Science Mathematics Medicine