Robust, Long-Term, and Exceptionally Sensitive Microneedle-Based Bioimpedance Sensor for Precision Farming
AuthorsBu Khamsin, Abdullah
Salama, Khaled N.
KAUST DepartmentElectrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Mechanical Engineering Program
Physical Science and Engineering (PSE) Division
Biological and Environmental Sciences and Engineering (BESE) Division
Desert Agriculture Initiative
Permanent link to this recordhttp://hdl.handle.net/10754/669704
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AbstractPrecision farming has the potential to increase global food production capacity whilst minimizing traditional inputs. However, the adoption and impact of precision farming are contingent on the availability of sensors that can discern the state of crops, while not interfering with their growth. Electrical impedance spectroscopy offers an avenue for nondestructive monitoring of crops. To that end, it is reported on the deployment of impedimetric sensors utilizing microneedles (MNs) that can be used to pierce the waxy exterior of plants to obtain sensitive impedance spectra in open-air settings with an average relative noise value of 3.83%. The sensors are fabricated using a novel micromolding and release method that is compatible with UV photocurable and thermosetting polymers. Assessments of the quality of the MNs under scanning electron microscopy show that the replication process is high in fidelity to the original design of the master mold and that it can be used for upward of 20 replication cycles. The sensor's performance is validated against conventional planar sensors for obtaining the impedance values of Arabidopsis thaliana. As a change is detected in impedance due to lighting and hydration, this raises the possibility for their widespread use in precision farming.
CitationBukhamsin, A., Moussi, K., Tao, R., Lubineau, G., Blilou, I., Salama, K. N., & Kosel, J. (2021). Robust, Long-Term, and Exceptionally Sensitive Microneedle-Based Bioimpedance Sensor for Precision Farming. Advanced Science, 2101261. doi:10.1002/advs.202101261
SponsorsThis work was funded and supported by King Abdullah University of Science and Technology (KAUST). The authors would like to thank the Center for Desert Agriculture at KAUST for their assistance.
CollectionsArticles; Biological and Environmental Science and Engineering (BESE) Division; Physical Science and Engineering (PSE) Division; Electrical and Computer Engineering Program; Mechanical Engineering Program; Desert Agriculture Initiative; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Except where otherwise noted, this item's license is described as This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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