| Literature DB >> 35712566 |
Mervin Chun-Yi Ang1, Tedrick Thomas Salim Lew2,3.
Abstract
As global population grows rapidly, global food supply is increasingly under strain. This is exacerbated by climate change and declining soil quality due to years of excessive fertilizer, pesticide and agrichemical usage. Sustainable agricultural practices need to be put in place to minimize destruction to the environment while at the same time, optimize crop growth and productivity. To do so, farmers will need to embrace precision agriculture, using novel sensors and analytical tools to guide their farm management decisions. In recent years, non-destructive or minimally invasive sensors for plant metabolites have emerged as important analytical tools for monitoring of plant signaling pathways and plant response to external conditions that are indicative of overall plant health in real-time. This will allow precise application of fertilizers and synthetic plant growth regulators to maximize growth, as well as timely intervention to minimize yield loss from plant stress. In this mini-review, we highlight in vivo electrochemical sensors and optical nanosensors capable of detecting important endogenous metabolites within the plant, together with sensors that detect surface metabolites by probing the plant surface electrophysiology changes and air-borne volatile metabolites. The advantages and limitations of each kind of sensing tool are discussed with respect to their potential for application in high-tech future farms.Entities:
Keywords: nanosensors; non-destructive; plant health; volatiles; wearable sensors
Year: 2022 PMID: 35712566 PMCID: PMC9197209 DOI: 10.3389/fpls.2022.884454
Source DB: PubMed Journal: Front Plant Sci ISSN: 1664-462X Impact factor: 6.627
Comparison of the various in vivo electrochemical and plant nanobionic sensors.
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| H2O2 | Indium tin oxide | Nano-gold | Voltammetry | Tomato leaves | Sun et al., |
| SA | Carbon tape | Multi-walled carbon nanotubes/Nafion | Voltammetry | Tomato leaves | Sun et al., |
| Tryp | Glass carbon | Polydopamine/reduced graphene oxide/MnO2 nanocomposite | Voltammetry | Tomato fruits | Gao et al., |
| Tryp | Miniaturized graphite rod | Multi-walled carbon nanotubes/poly(sulfosalicylic acid) | Voltammetry | Tomato and cherry tomato fruits | Yang et al., |
| ABA | Ta wires | Vertical graphene with core-shell Au@SnO2 nanoparticles assembled onto microneedle array | Chronocoulometry | Cucumber fruits and juices, grapes and radishes, blended Arabidopsis leaf juices | Wang et al., |
| SA | Al microelectrodes | Core-shell Au@Cu2O nanoparticles, graphene and polydopamine densely packed into IDME array | Chronocoulometry | Cucumber leaves, juices and stems | Liu et al., |
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| H2O2 | HiPco SWNT and (6,5)-enriched SWNT | Single-stranded DNA oligomer: (GT)15 | nIR fluorescence quenching | Lettuce, Arugula, Spinach, Strawberry blite, Sorrel, Arabidopsis thaliana leaves | Lew et al., |
| NAA | HiPco SWNT | Cationic poly(N-vinyl imidazolium) | nIR fluorescence quenching | Spinach, Arabidopsis thaliana, Pak choi, Rice leaves | Ang et al., |
| 2,4-D | HiPco SWNT | Cationic fluorene-co-phenyl polymer | nIR fluorescence turn-on | Spinach, Arabidopsis thaliana, Pak choi, Rice leaves | Ang et al., |
| Tannic acid | Monochiral (6,5) SWNT | Polyethylene glycol–phospholipids | nIR fluorescence red-shift and quenching | Soybean Glycine suspension cells, Soybean seedling root exudates, Tococa leaf methanol extracts | Nißler et al., |
| As (III) | HiPco SWNT | Single-stranded DNA oligomer: (GT)5 | nIR fluorescence turn-on | Spinach, Rice and Pteris cretica hyperaccumulator fern leaves | Lew et al., |
| Picric acid | HiPco SWNT and (6,5)-enriched SWNT | Bombolitin II peptide | nIR fluorescence quenching | Spinach leaves | Wong et al., |
Figure 1(A) Paper-based electro-analytical device used in detection of H2O2 in circular plant samples punched out of the tomato leaves (Sun et al., 2020); (B) Miniaturized electrochemical sensor inserted into tomato fruits for detection of auxin precursor, Tryp (Yang et al., 2021); (C) in situ ABA electrochemical sensor assembled onto a microneedle array for detection in fruits (Wang et al., 2021); (D) Current-time curves generated when the ABA microneedle sensor is inserted into cucumber where ABA concentrations is linearly correlated with the step current observed (Wang et al., 2021); (E) in situ SA electrochemical sensor arranged in an IDME array for insertion into cucumber leaves (Liu et al., 2021); (F) Response current (top) and derived SA concentration (bottom) obtained from the IDME array sensor in 5 different live cucumber leaves (Liu et al., 2021); (G) Brightfield (left) and corresponding false-colored images (right) of a spinach leaf infiltrated with H2O2 (red arrow) and reference (blue arrow) nanosensors on both sides of the leaf mid-vein. False-colored images shows the transient H2O2 wave upon mechanical wounding of the leaf at t = 0 min (Lew et al., 2020b); (H) H2O2 nanosensor response to different types of stress applied to the plant, including mechanical wounding (red), flg22 treatment (green), high light (orange) and high heat (blue) stresses (Lew et al., 2020b); (I) Real-time sensing of 2,4-D uptake in hydroponically grown pak choi and rice leaves using nanosensors which illustrated a turn-on response observed in pak choi but not in rice over a time-period of 5 h (Ang et al., 2021); (J) Arsenite nanobionic sensor infiltrated into hyperaccumulator plant Pteris creticas fern, showing intensity changes corresponding to arsenic accumulation detected over 7-day time period upon arsenite exposure (Lew et al., 2021); (K) Schematic of standoff detection of nitroaromatic compound, picric acid, using nanosensors with real-time information relayed from the nanosensor-infiltrated plant to a portable Raspberry Pi-based electronic device (Wong et al., 2017).
Figure 2(A) Thermogel application to monitor electrical potential signals from plants with hairy stems (Luo et al., 2021). (B) Printed conductive polymers enabled impedance spectroscopy to detect ozone damage (Kim et al., 2020). (C) Detection of plant VOCs using smartphone-integrated chemical sensor arrays (Li et al., 2019). (D) Differential colorimetic response of sensor arrays upon exposure to tomato plants infected with Pseudomonas infestans (Li et al., 2019). (E) Principal Component Analysis (PCA) plot to distinguish pathogenic infections on tomato plants based on chemical sensor arrays (Li et al., 2019).