Optimizing Low Power Near L-band Capacitive Resistive Antenna Design for in Silico Plant Root Tomography Based on Genetic Big Bang-Big Crunch

Root system architecture (RSA) phenotyping is essential in formulating suitable organic fertilizers, irrigation, and protective regiments concerning its functional role in resource acquisition for plant growth. However, Ground Penetrating Radar, and Magnetic Resonance, Positron Emission, and X-Ray Micro Computed Tomography Scanning have high power requirements, and RGB imaging demands an intrusive scheme. Existing antenna-based imaging systems are not intelligently optimized yet. To address these challenges, this study developed a low power (10 W) near L-band capacitive resistive antenna system for in silico maize root tomography optimized using three novel advanced evolutionary computing, namely, Genetic Particle Collision Algorithm (gPCA), Genetic Integrated Radiation Algorithm (gIRA), and Genetic Big Bang-Big Crunch Algorithm (gBB-BC). Two capacitive resistive antenna designs were developed using CADFEKO: single parallel plate and 90-electrode dipole-dipole, where root information acquisition and processing from healthy maize seedling inside a PVC pipe intact with soil were done. Maize root permittivity and soil quality were set to resemble actual biological experiments. Transmitter frequency was determined using multigene (10 genes) genetic programming (MGGP) integrated with PCA, IRA, and BB-BC to determine the global maximum voltage at the receiver dipole. Based on in silico experiments, gBB-BC resulted in 0.984463 GHz operating frequency that lies within the global solutions of gPCA (> 1 GHz) and gIRA (< gBB-BC). The root tomography generated from electric field mapping using the gBB-BC-based antenna exhibited more pronounced RSA, while gIRA-based antenna is sensitive only to root tips. Hence, the established root imaging protocol here supports faster, low-power, and non-destructive approaches.