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.

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

Land surveying has been one of the core operations in performing underground imaging. It is known that dynamic and continuous resistivity readings were employed through this technique using the array of capacitive electrodes being towed with a light vehicle. However, the main challenge in doing subsurface surveying is the change in speed of the system when there are inevitable obstacles and sloping road surfaces. To address it, this study will develop prediction models using different computational intelligence such as multigene symbolic regression genetic programming (MSRGP), regression-based decision tree (RTree), and feed forward neural network (FFNN) that will result in a smart speed controller system that can maintain the constant speed of the towed subterranean system. The best performing prediction model will be considered as the SpeedX. The expected output is a correction factor that will signal the speed controller in slow down or inclined plane road environment to maintain a constant speed of 1.6667 m/s for avoidance of data distortion on land surveying. Thus, the MSEs for MSRGP, FFNN, and RTree are 0.00163, 0.00178, and 0.00240, respectively. This results in MSRGP as the best performing model and was considered as the SpeedX model. Other evaluation metrics were employed such as the MAE and R2 which signify the advantage of SpeedX. Furthermore, the comparison between the CI-controlled and uncontrolled towed subterranean imaging trailer system, as well as its advantages clearly highlight the advantage of embedded SpeedX in the system.

SpeedX: Smart Speed Controller Model of Towed Subterranean Imaging System for Resistivity Data Distortion Reduction Using Computational Intelligence

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. 

Because of the mobile nature of Capacitive Resistivity Imaging (CRI) towed arrays for subsurface surveys, the positions of its channels should be accurately recorded, as this is needed during the inversion process to create accurate resistivity maps. While CRI equipment that uses GPS to determine the positions is common and easy to implement, it is important to apply filtering methods to the obtained GPS coordinates to minimize the errors generated by the sensor. This study aimed to address the problem by applying the Savitzky-Golay filter to perform position estimation from noisy GPS measurements. The approach was tested in a virtual setup that uses CoppeliaSim for simulating the movement of the CRI towed array during a survey and a python script that records the GPS measurements from CoppeliaSim and applies the proposed filter to provide the position estimates. The script also generated sample resistivity maps given the noisy and estimated positions of the channels. Results showed that the Savitzky-Golay filter minimized each channel's positional errors during the 30 simulation runs to a mean error of 0.777m at most or a maximum of 61.62% decrease. Likewise, the importance of minimizing the position errors in generating the resistivity maps was proved as using the filter resulted in a more correct imaging of the subsurface for detecting the relevant regions and anomalies. 

Robotic simulation and GPS position estimation of a towed CRI equipment using Savitzky-Golay filter

The capacitive resistivity technique in underground object detection comprises configured transmitter and receiver antennas that are capacitively coupled to the ground. However, underground imaging lacks a basis for determining the received voltage for precise data analysis. This study aimed to develop a prediction model of the received input voltage signal amplitude from the ground of a single-pair antenna underground imaging system. The receiver antenna circuit for this application is designed and simulated in Proteus Software. Genetic Programming (GP) is applied to predict the received input signal based on the shape of the received waveform signal, operating frequency, resistance of the waveform shaping circuit, and buffer amplifier output signal. The resulting fitness function of GP (4) is acceptable as it scored an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 99.38% with a negligible MSE of 0.0059 and an MAE of 12.3423. Then, the GP fitness function is optimized through Genetic Algorithm (GA), Differential Evolution (DE), and Evolutionary Strategy (ES) in which the GP-GA model outperformed the two hybrid models providing fast convergence and 2.49e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−8</sup> best fitness value. This study proved that GP can be effectively combined with stochastic genetic evolution algorithms to avoid lengthy mathematical calculations and accurately estimate the natural voltage received from the ground. 

Hybrid Stochastic Genetic Evolution-Based Prediction Model of Received Input Voltage for Underground Imaging Applications

Capacitive resistivity subsurface imaging of roads operating at very low frequency is susceptible to antenna characteristic capacitance dynamics that may cause unwanted signal reflection, coupling, and unfavorable effect on reception sensitivity. Antennas are conventionally modeled using a complex and repetitive default mathematical method that is prone to human error and discrete results. To address this emerging challenge, this study has developed a new technique for plate-wire antenna capacitance optimization through equatorial dipole-dipole antenna geometry modeling using genetic programming (GP) integrated with metaheuristic methods, namely Archimedes optimization algorithm (AOA), Lichtenberg algorithm (LA), and Henry gas solubility optimization (HGSO). GP was used to construct the antenna capacitance fitness function based on 241 combinations of wire antenna radius and elevation, and dipole plate elevation, length, width, and thickness measurements. Minimization of antenna capacitance (approaching 1 nF) to achieve quasi-static condition was performed using GP-AOA, GP-LA, and GP-HGSO. The 3 metaheuristic-based antennas were 3D-modeled using Altair Feko and compared from the default antenna’s electrical features. It was found that even with the smallest dipole geometry, hybrid GP-LA antenna model exhibited the most practical outputs at 5 kHz with correct directional propagation based on its radiation pattern, a realistic receiver voltage of -8.86 dBV which is close to the default model, and a high-power efficiency of 99.925%. While hybrid GP-AOA and GP-HGSO resulted in indirect coupled transceiver systems with unsuitable antenna characteristic capacitance inducing anomalous receiver voltages. The experimental results prove the validity of the developed technique for more accurate determination of optimal antenna geometry.

Optimization of Subsurface Imaging Antenna Capacitance through Geometry Modeling using Archimedes, Lichtenberg and Henry Gas Solubility Metaheuristics

Colonization of man surpasses the tropospheric atmosphere of Earth and is targeting to create new generation of people in space, particularly Mars, in the future. This relies on the metabolic needs which include oxygen for breathing, and water and food for astronaut diet. The key aspect to achieve this goal is to employ sustainable agriculture even inside a spacecraft as it requires additional food supply when transporting more people from Earth to Mars in this case, given longer travel time is expected. Terrestrial-based agricultural technologies that are currently being utilized are brought by Industrial Revolutions (IR) 4.0 and 5.0. However, the knowledge on the interventions of IR 5.0 technologies is still fractional in relation to its potential for space farming. This study discusses the comparative differences of IR 4.0 and 5.0 in relation to its impacts to farm labor, cultivation and post-harvest system, green products and services, and agricultural supply chain; a systematic analysis of space farms on spacecraft and planetary surfaces as the exact future of farming; the dissection of foreseeable challenges and the roles of government, private industries and academic and research institutions for interleaving IR 5.0 and space agriculture; and the future directives concerning IR 5.0 technologies as tools for bioregenerative life support system and circular manufacturing. Here, the context of sustainable space agriculture is limited only to the adaptable technologies. Hence, this study established a framework that IR 5.0 can boost sustainable space farming in terms of adaptive cropping system, robot-based operations, and carbon neutrality.

A Look at the Near Future: Industry 5.0 Boosts the Potential of Sustainable Space Agriculture

Root foraging is affected by environmental stimuli that dictates its propagation vector. Multiple-root interaction has the potential to provide relevant information about asynchronous systems like the natural soil system. In this study, tomato root was used as the information producing element in three fabricated three-input-three-output negative chemotropism root logic gates: synchronized (rNHG1), and desynchronized (rNHG2 and rNHG3). Specific output channels in rNHG2 and rNHG3 were fertigated with highly acidic sodium chloride solutions (30 mM and 75 mM) and only one channel was enriched with control solution providing additional desynchronization in the system computing the logic functions of $(x,y,z) = (y + z + x\bar z)$ for rNHG1, $(x,y,z) = (xy + xz,y + z + x\bar z)$ for rNHG2, and $(x,y,\bar z) = (\bar xyz + x\bar yz,y + z + x\bar z)$ for rNHG3 where x, y, and z represent the presence of root in the output channel, whereas negated variables indicate the absence of root. Overall, root channels fertigated with control nutrients for all root logic gates resulted in the same Boolean function and negative halotropism not only repelled the growth direction of root, but it also reduced the radicle, xylem, and phloem diameters. As the root channels become more desynchronized, root fresh and dry weights decreased due to energy utilization in bending response. Here, it was proven that plant roots manifest computation and signaling based on repellant chemical. Hence, the developed root logic provides valuable information that can be extended in developing plant root-based robotic applications.

Negative Halotropism Expressed by 3-input-3-output Tomato Root Arithmetic-Logic Gate

Screen-printed graphite electrode on polyvinyl chloride and parchment strips integrated with genetic programming for in situ nitrate sensing of aquaponic pond water

Jonah Jahara Baun

Papers

Optimization of Subsurface Imaging Antenna Capacitance through Geometry Modeling using Archimedes, Lichtenberg and Henry Gas Solubility Metaheuristics

A Look at the Near Future: Industry 5.0 Boosts the Potential of Sustainable Space Agriculture

Negative Halotropism Expressed by 3-input-3-output Tomato Root Arithmetic-Logic Gate

Screen-printed graphite electrode on polyvinyl chloride and parchment strips integrated with genetic programming for in situ nitrate sensing of aquaponic pond water