Seed Assessment Using Fuzzy Logic and Gas Discharge Visualization Data

Abstract

Assessment of sowing material is a significant concern in seed science. A promising tool for assessing seed material is Corona Discharge Photography or Gas Discharge Visualization (GDV). In this study, this tool was applied to determine relationships between sowing material characteristics and GDV parameters; an Adaptive Neuro-Fuzzy Inference System (ANFIS) was utilized to interpret the experimental data. By using ANFIS, a three input fuzzy inference system was constructed to define the contiguous relations between GDV parameters (i.e., glow area and shape factor) and root length.