Understanding the processes governing plant growth and response of the latter at different stress (water, heat or drought,…) are fundamental in order to improve and better adapt plant in their fluctuating environment (mainly rice, sorghum, sugar cane and oil palm in our case). Modeling and simulation of such plant complex models allow testing, in silico, different assumptions about the processes controlling plant growth. There are already many models of plants that all have their strengths and weaknesses. These include, for example: STICS, GreenLab, APSIM, DSSAT, Sunflo, SarraH, EcoMeris- tem,… In these models, behaviors, or reactions, were activated, typically by functions (or equations) with thresholds which allow trigger behavior with greater or lesser intensity levels. For example: destruction of a sheet, carbonaceous material reallocation, etc. Now it appears (according to knowledge given by ecophysiological expert) that in natural systems, this anal- ogy is not always true. Indeed, these systems, a plant for example, are constantly in a steady state while trying to reach their final goal that is growing on order to produce. Due to these facts, one can realize that there are al- ways adjustments between the different organs of the plant. Unfortunately, the above conventional approaches used so far does not allow to take into account this fact, let alone implement them. Also, the objective of this work is to try to fill this gap in our plant models. To do so, we should focus on the elementary bricks (or organs) within a plant: leaves, between node axis, tiller, etc. and describe individual behavior and interactions. Naturally enough, one can imagine that the multi-agent systems, from distributed artificial intelligence, are a good candidate to represent these phenomena. To do this, we decomposed the plant into six agents: culm, root, leaf, internode, panicle, and peduncle. Then the plant is seen as a society of such agents. The culm agent's behavior is to stand