Fuzzy Mathematical Model of Photosynthesis in Jalapeño Pepper

Abstract

Photosynthesis is one of the essential processes for life on the planet. Photosynthesis cannot be measured directly because this complex process involves different variables; therefore, if some variables of interest are integrated and measured, photosynthesis can be inferred through a mathematical model. This article presents a fuzzy mathematical model to estimate photosynthesis. This approach uses as input variables: Soil moisture, ambient temperature, incident radiation, relative humidity, and leaf temperature. The fuzzy system was trained through data obtained from experiments with jalapeño pepper plants and then validated against the LI-COR Li-6800 equipment. The correlation coefficient (R2) obtained was 0.95, which is a higher value than some published in the literature. Based on the Takagi-Sugeno method, the proposed model was designed and implemented on the MATLAB platform using ANFIS (adaptive neuro-fuzzy inference system) to determine the parameters, thus achieving a high-precision model. In addition, the fuzzy model can predict photosynthesis at different temperature changes, soil moisture levels, and light levels. The results of this study indicate the possibility of modeling photosynthesis using the fuzzy logic technique, whose performance is much higher than other methods published in recent articles.