Crop yield prediction is a key challenge in plant science, requiring the integration of heterogeneous experimental data to extract predictive biological knowledge. In this study, we present a machine learning framework for crop yield prediction, combining phenotypic, environmental, and molecular gene expression data with yield outcomes. Using multi-season, multi-location field trials, we construct a unified dataset that captures plant development through time-resolved measurements of canopy dynamics, environmental conditions, and final performance.
We apply structured feature engineering tailored to each data type, including transformation, standardization, and aggregation of time-resolved measurements across developmental stages. These representations capture developmental and environmental variation in a form suitable for prediction, including biologically meaningful derived variables. Machine learning models trained on these features achieve strong predictive performance, with canopy development and environmental signals contributing most to accuracy, while molecular data provides complementary insights.
Beyond predictive performance, this study demonstrates how heterogeneous plant data can be integrated into reusable analytical frameworks that support biological interpretation and hypothesis generation. The results highlight the value of preserving experimental context and enabling consistent comparisons across experiments, which is essential for comparative and meta-analyses in plant science. These representations also support the development of models that can be transferred beyond a single study, facilitating hypothesis generation and exploration of plant responses under varying environmental conditions.
This approach aligns with the ELIXIR Plant Sciences Community, which promotes the integration of heterogeneous plant data for interoperable analysis and modelling in plant science.

Co-authors: Angelika Vižintin, Eva Turk, Maja Križnik, Marko Petek, Guus Heselmans, Anže Županič, Robert Graveland, Christian Bachem, Markus Teige, Bianca Doevendans, Alexandra Ribarits, Kristina Gruden, Jan Zrimec