With over 300 000 species on Earth, the world of flowering plants is incredibly diverse. But, despite this diversity, plant structure is built from the same basic botanical elements, leaves, stems, fruits, roots, etc., whose functions are unchanged as sources and sinks for biomass production and partitioning.

However, to understand such a complex system as plant growth and development, knowledge about botany (plant structure organization) and physiology (action of environmental parameters, water transport, respiration, and photosynthesis) is not sufficient. Moreover, statistical correlations and empirical functions between measured variables such as temperature, light, plant height or weight are also not sufficient mathematically speaking, since these measures are themselves resulting of a complex functioning that involves the hidden parameters of an underlying mathematical model that is to be built.

Identifying the relevant parameters that bridge together the various disciplines results in translating the reality into a mathematical model, that will allow plant behavior study. The complexity of the real world must be dropped down in order to build efficient models. This can be achieved by a dialog between biologists, who have the qualitative knowledge, and mathematicians, who handle quantitative relationships through equations.

Computing the numerical values of the hidden parameters from measurements on real plant architecture is what is called an inverse problem. Engineering applications for agriculture can be worked out only if this problem is solved.
Plant architecture results from both meristem functioning (organogenesis) and photosynthesis (biomass production and partitioning) and it may be assumed that plant architectural development concerns the growth process trajectory keeping at any time in its memory the underlying structure. Thus the hope is to trace back the growth process from measurements on plant architecture in given environmental conditions, and furthermore to control the plant behavior from acting on the environmental parameters.

Finally, the research work may be considered as fulfilled if it is possible to simulate the growth process with a minimum number of parameters and to build step by step the plant architecture with all the organs in the right place inside the plant structure and with the correct biomass content. This is the goal of a computerized implementation. Such important problems as optimizing the use of resources (water, fertilizers in fields, temperature and light in glasshouses) and cultivation systems (planting density, pruning) can then be successfully solved.

This resource presents an efficient dynamical plant growth and architecture model built on the basic knowledge coming from botany, ecophysiology, agronomy, applied mathematics, and computer science.