Plant Computational Modelling of Green Amaranth Plant for Investment Analyst using FSPM-GroIMP and Fuzzy Logic

[摘要] Functional-Structural Plant Modelling (FSPM) isplant modeling that considers morphology, physiology, and statistical data. Oneof the software for FSPM is the Growth-Grammar Interactive Modelling Platform (GroIMP).GroIMP is a Java-based platform using the XL language, which is a languagedeveloped from System-L. This study aims to build a virtual plant model ofgreen amaranth (Amaranthus Sp.) in a hydroponic planting systemfor strategic decision-making. In this study, parameter analysis and modeldevelopment were carried out. The model is divided into two sub-model blocks byoperating three methods. There are two sub-models, namely the sub-model virtualplant, and the sub-model fuzzy logic. In the virtual plant model usingmathematical modeling and FSPM-GroIMP methods, the results of this model arethe growth pattern of green amaranth to harvest, the weight of green amaranth,and visualization of hydroponic green amaranth plants. In comparison, thesub-model fuzzy logic uses the fuzzy logic method to predict the selling priceof plants and expenses. The two sub-models are combined to produce hydroponicgreen amaranth strategic decisions. The output of strategic decisions is adecision that has an impact on investment value. The investment value ismeasured by the CR Ratio, which is the investment value by comparing the costwith the revenue from hydroponic green amaranth. Other results showed that theaverage weight of a single green amaranth plant is 11.85 g and the RC ratiovalue is 4.5 for 500 plants with good plant quality, a fixed cost of IDR30,000,variable cost is IDR20,000 and the price is IDR57,282 per kilogram.Calculations of model verification and validation were carried out andindicated the built model according to the existing theory had the correctvariable values.

[学科分类] 计算机科学（综合）

[关键词] Plant Modelling;Functional-Structural Plant Modelling;Growth Grammar Interactive Modelling Platform;Decision Support Model;Fuzzy Logic;RC Ratio