Hybrid Machine Learning process-based modelling approaches for climate adaptation strategies (KB-46-005-024)

The aim of this proposal is to develop and apply hybrid approaches based on ML and process-based modelling for the assessment of climate adaptation strategies for different actors in the agri-food value chain. This project is built on the following four work packages:

 

WP1: data standardization through the development of ontologies and integrated knowledge graphs to harmonize terminology and data sources. The WP focused on knowledge graphs for interoperability of data from different domains.

WP2: development of hybrid ML and process-based models for crop growth from time series data that involve essential genotype-by-environment interactions to describe crop responses to environmental stresses (e.g. drought). This WP focused on hybrid ML models based on the integration of synthetic data from crop growth models (Tipstar, APSIM) and physics informed neural network to predict yield and crop stresses

WP3: pest prediction models using ML and will focus on the development of ML models for yellow stem borer infestation on rice in India.

WP4: assessment of post-harvest tomatoes quality, this WP will focus on the comparison of modelling approaches of different complexity from linear models to hybrid ML and the relation between complexity and model fitness.