engleski

Development of phytotoxic natural molecules as complementary herbicidal agents is supported by machine learning study

Machine learning (ML) approaches are widely used to analyse and model various types of problems, including those related to food and climate changes. The basic premise is to have a large and reliable dataset which is used for training and testing predictive model. In the lecture, application of ML in the food field will be presented through the results of ML study performed for herbicides. Very extensive and wide use of herbicides leads to increasing of (i) weed resistance and (ii) human health issues. We have applied ML approaches (Random Forest, clustering) for developing predictive models for mode of action and weed selectivity of herbicides grouped in HRAC/WSSA list (https://github.com/mlkr- rbi/Herbicide-Classification). Our ML study points to shortcomings of usage rotation strategy which is based exclusively on HRAC/WSSA classification for reducing weed selectivity. In addition, phytotoxic natural molecules are identified as different in chemical as well as biological space in comparison to synthetic herbicides and thus their usage and development may provide a complementary way to slow down weed resistance.

herbicides ; machine learning ; natural products ; weed resistance

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