Decoding living systems: Reassessing crop model frontiers via biological dynamics and optimized phenotype
Edgar S. Correa, Paulo Eduardo Teodoro, Paulo Eduardo Teodoro, Paulo Eduardo Teodoro, Paulo Eduardo Teodoro
TL;DR
This paper introduces a framework to model and optimize crop performance by integrating biological processes and AI, revealing strategies for yield under varying water conditions.
Contribution
A novel inverse engineering framework combining sensitivity analysis, genetic algorithms, and similarity analysis to optimize crop phenotypes.
Findings
Eight genetic coefficients were identified as key yield drivers with consistent rankings.
Two adaptive strategies were found: extended growth under high water and shortened cycles under water deficit.
WAB56−50 and DKAP2 were identified as top breeding candidates with a 22–30% genetic gap to computational optima.
Abstract
Modeling and optimizing phenotypic performance of biological systems demands understanding how physiological processes mediate genotype-by-environment interactions. While AI-driven approaches achieve predictive accuracy, they often function as black boxes that obscure biological causality. Process-based models address this limitation through explicit mechanistic representation, enabling both quantitative optimization and biological interpretation. This study contributes an inverse engineering framework with three integrated layers: sensitivity analysis validating biological coherence, genetic algorithm exploring virtual phenotypes to identify adaptive strategies, and similarity analysis quantifying routes from computational optima to field-validated cultivars. Sensitivity analysis identified eight genetic-based coefficients governing yield with robust rankings (95% CI width = 0.04). The…
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Taxonomy
TopicsGreenhouse Technology and Climate Control · Climate change impacts on agriculture · Plant Molecular Biology Research