Integration Fuzzy System into Functional Structural Plant Model Based Environment Conditions

The role acquired by modeling in plant sciences includes integration of knowledge, exploration of the behavior of the plant system beyond the range of conditions covered experimentally and decision support. The purpose of the model determines its structure. Initially process artificial intelligence (PAI) were developed separately from structural (or: architectural or morphological) plant models (SPM). Combining PAI and SPM into functional structural plant models (FSPM) or virtual plants has become intelligence. This adds a dimension to classical growth modeling. FSPM are particularly suited to analyze problems in which the spatial structure of the system is an essential factor contributing to the explanation of the behavior of the system of study. Examples analyses of mechanisms of physiological response to environmental signals that affect plant architectures on production of the plant architecture (stalk, branch, leaf and bloom) in the plant. To make the condition close to the real environment characteristic, it is required the axiom and syntax grammar for the L-System. In this paper, we propose the use of fuzzy system together with the L-System method, to model the plant growth based on the current environment condition. At the beginning of plant growth, let the sprout of plant initially be denoted as axiom. This characteristic rules are illustrated in the reproduction of L-System also occur in nature of plant growth conditions based on fuzzy system. The software used in this modeling is GroIMP. The plant architecture value is given based on the fuzzy system, the plant growth is visualized with the L-System method, controlled evolution of complex structures is exemplified by the development of tree structures generated by the movement of a 3D-turtle and the 3-Dimension graph is shown as the virtual plant growth. Good modeling practice involves different steps in model development. These steps are discussed and include the conceptual modeling, data collection, model implementation, model verification and evaluation, sensitivity analysis and scenario studies.