African agriculture, the pillar of food security and the economy for millions of people, is on the front line when it comes to climate change. Rising temperatures, more frequent droughts and variable rainfall are threatening the yields of traditional food crops. Conventional agronomic approaches are proving inadequate in the face of this challenge. This article proposes a paradigm shift by positioning functional-structural plant models (FSPMs), coupled with artificial intelligence (AI) methods, as a strategic tool for accelerating the adaptation of African agricultural systems. We present an integrated methodological framework (FSPM-AI) that starts with the collection of high-precision agro-physiological data in the field and culminates in the creation of "digital twins" of plants (i.e., dynamic virtual replicas of real plants). These models make it possible to simulate the complex interaction between a plant’s genetics, its 3D architecture, its physiology (particularly photosynthesis), and its environment. Using AI techniques for model calibration, sensitivity analysis, and simulation of future climate scenarios (e.g., representative concentration pathways 4.5/8.5), this approach makes it possible to (1) identify the key morpho-physiological traits that confer resilience (e.g., leaf angle, biomass allocation, and photosynthetic efficiency), (2) optimise cultivation practices (density and fertilisation), and (3) guide varietal selections. Using C4 cereals such as millet and sorghum, which are essential for arid areas but scientifically under-modelled, as an example, we illustrate how this coupling of modelling and AI can fill a critical knowledge gap and provide robust decision support tools for farmers, researchers, and policy makers in Africa.