Mechanistic crop modelling and AI for ideotype optimization: Crop-scale advances to enhance yield and water use efficiency

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 genetic algorithm explored 5,364 virtual cultivars across 40 generations, revealing two strategies: extended growth (116 days) achieving 4,837 kg/ha under higher water availability (815 mm, field capacity 0.30), and shortened cycles (100-103 days) maintaining high efficiency (HI: 0.55-0.58) under water deficit (540 mm, field capacity 0.23)--covering 89% of the cultivation area. Similarity analysis against 21 field-validated cultivars identified WAB56-50 (70.7%) and DKAP2 (67.2%) as breeding candidates, quantifying a 22-30% genetic gap between current germplasm and computational optima. The framework, built upon 3 years of field characterization, compressed the evaluation and selection cycle, enabling adaptation across regional precipitation gradients identified through GMM-based classification. The principles demonstrated here extend across biological scales--from organismal phenotyping to cellular systems where biological dynamics can be modeled and traits measured. Author SummaryFrom cells to organisms, living systems respond to environmental constraints through complex interactions between genetic potential and physiological processes. Deciphering these dynamic interactions is a fundamental challenge across the life sciences. Biological process-based modeling through engineering and AI approaches, such as pattern recognition, dynamic modelling, and image processing, has the potential to advance this frontier. Applications range from crop resilience under climate change to cellular stress responses and broad implications across biology and medicine. Agriculture exemplifies this challenge: adapting to climate change, and resource scarcity demands rapid phenotyping of large populations to identify promising genotypes. This research advances a broader vision: mechanistic modeling can transcend its conventional predictive role to become a quantitative design framework for target adaptation strategies and causal interpretation of complex biological patterns. The work contributes to this vision through an inverse engineering framework integrating three layers--sensitivity analysis identifying control points, genetic algorithm optimization exploring virtual phenotypes, and genotypic-based validation mapping implementation routes--providing targeted recommendations without decade-long empirical cycles. While process-based models may not yet fully capture genetic complexity at gene network or 3D architectural levels, their capacity to represent functional diversity remains a powerful asset. The principles demonstrated here are scale-independent. What changes across biological scales is not the analytical logic but the resolution of observation. Bridging this gap--from crop canopy to cellular architecture, from field phenotyping to subcellular dynamics--represents both the challenge and the opportunity for quantitative biology driven by technological advances and AI in the coming decade.