Research Paper:
Non-Invasive Plant Root Tomography Through Optimized Sonar Array Transducer Antenna Design Using Genetic Swarm Metaheuristic
Jonnel D. Alejandrino*,, Ronnie S. Concepcion II** , Argel A. Bandala* , Edwin Sybingco** , Ryan Rhay P. Vicerra** , and Elmer P. Dadios**
*Department of Electronics and Computer Engineering, De La Salle University
2401 Taft Avenue, Malate, Manila 1004, Philippines
Corresponding author
**Department of Manufacturing Engineering and Management, De La Salle University
2401 Taft Avenue, Malate, Manila 1004, Philippines
Plant root imaging is crucial for progress in various domains such as plant breeding and crop optimization. Traditionally, root tomography involves invasive methods that disrupt plant systems and yield non-reproducible results. As a result, non-invasive techniques, particularly electrical tomography, have gained significant attention. Despite the advantages, these techniques have limitations in terms of radiation efficiency and directivity due to suboptimal antenna design. This paper presents a comprehensive simulation on antenna design optimization focusing on dimensions, spacing, and integration of advanced algorithms. A micropatch transducer antenna was engineered for an existing in-silico plant root setup operating within a 3–5 MHz frequency range. The optimized dimensions of the antenna are 109.32 mm × 140.67 mm × 2.55 mm, and it resonates effectively within a frequency range of 3.1–5.68 MHz. Using scalar minimization techniques, patch transducers were interconnected into an antenna array with an optimized 3 mm spacing. Utilizing multi-objective optimization algorithm based on sperm fertilization procedure and shuffled frog leaping algorithm, optimal frequencies were obtained at 3,989,796.88 Hz and 3,989,951.83 Hz, respectively. Validated using CADFEKO software, the proposed antenna design demonstrated distinctive voltage distribution, superior directivity of 9.24 dBi, gain of 9.15 dBi, and 98.6% radiation efficiency when compared to the existing silicon-based root tomography antenna setups.
- [1] A. Li, L. Zhu, W. Xu, L. Liu, and G. Teng, “Recent advances in methods for in situ root phenotyping,” PeerJ, Vol.10, Article No.e13638, 2022. https://doi.org/10.7717/peerj.13638
- [2] J. D. Alejandrino et al., “fMaize: A Seamless Image Filtering and Deep Transfer EfficientNet-b0 Model for Sub-Classifying Fungi Species Infecting Zea mays Leaves,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.6, pp. 914-921, 2022. https://doi.org/10.20965/jaciii.2022.p0914
- [3] P. F. Zhao et al., “Electrical imaging of plant root zone: A review,” Computers and Electronics in Agriculture, Vol.167, Article No.105058, 2019. https://doi.org/10.1016/j.compag.2019.105058
- [4] S. Ehosioke et al., “Sensing the electrical properties of roots: A review,” Vadose Zone J., Vol.19, No.1, Article No.e20082, 2020. https://doi.org/10.1002/vzj2.20082
- [5] J. M. F. Custodio et al., “Acquisition of 3D Root System Simulation Parameters Using 2D Extracted Image Data and Genetic Programming,” Philipp J. Sci., Vol.151, No.6B, pp. 2355-2364, 2022. https://doi.org/10.56899/151.6B.05
- [6] R. Concepcion et al., “Optimizing Low Power Near L-Band Capacitive Resistive Antenna Design for in Silico Plant Root Tomography Based on Genetic Big Bang-Big Crunch,” Proc. of the 2023 17th Int. Conf. on Ubiquitous Information Management and Communication (IMCOM), 2023. https://doi.org/10.1109/IMCOM56909.2023.10035574
- [7] D. D. J. Corona-Lopez et al., “Electrical impedance tomography as a tool for phenotyping plant roots,” Plant Methods, Vol.15, No.1, Article No.49, 2019. https://doi.org/10.1186/s13007-019-0438-4
- [8] P.-F. Zhao et al., “Electrical imaging of plant root zone: A review,” Computers and Electronics in Agriculture, Vol.167, Article No.105058, 2019. https://doi.org/10.1016/j.compag.2019.105058
- [9] A. Li, L. Zhu, W. Xu, L. Liu, and G. Teng, “Recent advances in methods for in situ root phenotyping,” PeerJ, Vol.10, Article No.e13638, 2022. https://doi.org/10.7717/peerj.13638
- [10] J. M. Ladrido et al., “Comparative survey of signal processing and artificial intelligence based channel equalization techniques and technologies,” Int. J. of Emerging Trends in Engineering Research, Vol.7, No.9, pp. 311-322, 2019. https://doi.org/10.30534/ijeter/2019/14792019
- [11] J. Alejandrino et al., “A Hybrid Data Acquisition Model Using Artificial Intelligence and IoT Messaging Protocol for Precision Farming,” 2020 IEEE 12th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2020. https://doi.org/10.1109/HNICEM51456.2020.9400152
- [12] J. Alejandrino et al., “Cluster-Based Network for Improved Internet Resilience in Outland Agriculture,” 2021 IEEE 9th Region 10 Humanitarian Technology Conf. (R10-HTC), Bangalore, India, 2021. https://doi.org/10.1109/R10-HTC53172.2021.9641523
- [13] J. Alejandrino, “Remote monitoring system for outland precision farming,” Electronics and Communications Engineering Master’s Theses, De La Salle University, 2021.
- [14] J. Alejandrino et al., “Application-Based Cluster and Connectivity-Specific Routing Protocol for Smart Monitoring System,” 2020 IEEE 12th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2020. https://doi.org/10.1109/HNICEM51456.2020.9400107
- [15] J. Alejandrino et al., “Utilization of Self-Organizing Maps for Map Depiction of Multipath Clusters,” Springer, pp. 417-426, 2022. https://doi.org/10.1007/978-3-030-93247-3_41
- [16] J. Alejandrino et al., “Congestion Detection in Wireless Sensor Networks Based on Artificial Neural Network and Support Vector Machine,” 2020 IEEE 12th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2020. https://doi.org/10.1109/HNICEM51456.2020.9400062
- [17] J. D. Alejandrino et al., “Feasibility of Television White Space Spectrum Technologies for Wide Range Wireless Sensor Network: A Survey,” 2019 IEEE 11th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2019. https://doi.org/10.1109/HNICEM48295.2019.9072794
- [18] S. D. Assimonis et al., “A new high-gain and low-complexity pattern-reconfigurable antenna,” 9th European Conf. on Antennas and Propagation (EuCAP), 2015.
- [19] W. Liu et al., “Recent Advances in Design and Signal Processing for Antenna Arrays 2020,” Int. J. of Antennas and Propagation, Vol.2023, Article No.9843456, 2023. https://doi.org/10.1155/2023/9843456
- [20] J. Alejandrino et al., “Irescue: Tracking Device Using RuBee-Based Technology,” IEEE 13th Int. Conf. on Humanoid, Nanotechnology, Information Technology (HNICEM), Communication and Control, Environment, and Management (HNICEM), 2021. https://doi.org/10.1109/HNICEM54116.2021.9732045
- [21] J. D. Alejandrino et al., “Protocol-independent data acquisition for precision farming,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.4, pp. 397-403, 2021. https://doi.org/10.20965/jaciii.2021.p0397
- [22] J. D. Alejandrino et al., “chromoCorn: Zea mays Chromosome Classification Using Zero Order Fuzzy Inference System Based on Kernel’s Haralick Grey Level Texture Phenes,” 2022 IEEE 14th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2022. https://doi.org/10.1109/HNICEM57413.2022.10109415
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.