Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Nanobiotechnology approaches for engineering smart plant sensors

Abstract

Nanobiotechnology has the potential to enable smart plant sensors that communicate with and actuate electronic devices for improving plant productivity, optimize and automate water and agrochemical allocation, and enable high-throughput plant chemical phenotyping. Reducing crop loss due to environmental and pathogen-related stresses, improving resource use efficiency and selecting optimal plant traits are major challenges in plant agriculture industries worldwide. New technologies are required to accurately monitor, in real time and with high spatial and temporal resolution, plant physiological and developmental responses to their microenvironment. Nanomaterials are allowing the translation of plant chemical signals into digital information that can be monitored by standoff electronic devices. Herein, we discuss the design and interfacing of smart nanobiotechnology-based sensors that report plant signalling molecules associated with health status to agricultural and phenotyping devices via optical, wireless or electrical signals. We describe how nanomaterial-mediated delivery of genetically encoded sensors can act as tools for research and development of smart plant sensors. We assess performance parameters of smart nanobiotechnology-based sensors in plants (for example, resolution, sensitivity, accuracy and durability) including in vivo optical nanosensors and wearable nanoelectronic sensors. To conclude, we present an integrated and prospective vision on how nanotechnology could enable smart plant sensors that communicate with and actuate electronic devices for monitoring and optimizing individual plant productivity and resource use.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Nanobiotechnology approaches enable research and development of smart plant sensors that communicate plant chemical signals to agricultural and phenotyping equipment.
Fig. 2: Genetically encoded nanoscale sensors for plant signalling molecules have the potential to be delivered to plant genomes by engineered nanomaterials.
Fig. 3: Nanomaterial-based sensors allow in vivo optical monitoring of plant signalling molecules in real time.
Fig. 4: Nanotechnology-based flexible and wearable sensors for plant chemical sensing.
Fig. 5: Smart plant sensor communication and actuation of electronic devices through optical and radio waves, and electrical signals.

References

  1. 1.

    van Ittersum, M. K. et al. Can sub-Saharan Africa feed itself? Proc. Natl Acad. Sci. USA 113, 14964–14969 (2016).

    Google Scholar 

  2. 2.

    Tilman, D., Balzer, C., Hill, J. & Befort, B. L. Global food demand and the sustainable intensification of agriculture. Proc. Natl Acad. Sci. USA 108, 20260–20264 (2011).

    CAS  Google Scholar 

  3. 3.

    Joshi, R., Singla-Pareek, S. L. & Pareek, A. Engineering abiotic stress response in plants for biomass production. J. Biol. Chem. 293, 5035–5043 (2018).

    CAS  Google Scholar 

  4. 4.

    Suzuki, N., Rivero, R. M., Shulaev, V., Blumwald, E. & Mittler, R. Abiotic and biotic stress combinations. New Phytol. 203, 32–43 (2014).

    Google Scholar 

  5. 5.

    Fahad, S. et al. Crop production under drought and heat stress: plant responses and management options. Front. Plant Sci. 8, 1147 (2017).

    Google Scholar 

  6. 6.

    Mickelbart, M. V., Hasegawa, P. M. & Bailey-Serres, J. Genetic mechanisms of abiotic stress tolerance that translate to crop yield stability. Nat. Rev. Genet. 16, 237–251 (2015).

    CAS  Google Scholar 

  7. 7.

    de San Celedonio, R. P., Abeledo, L. G. & Miralles, D. J. Physiological traits associated with reductions in grain number in wheat and barley under waterlogging. Plant Soil 429, 469–481 (2018).

    Google Scholar 

  8. 8.

    Guillaume, C., Isabelle, C., Marc, B. & Thierry, A. Assessing frost damages using dynamic models in walnut trees: exposure rather than vulnerability controls frost risks. Plant Cell Environ. 41, 1008–1021 (2018).

    CAS  Google Scholar 

  9. 9.

    Chakraborty, S. & Newton, A. C. Climate change, plant diseases and food security: an overview: Climate change and food security. Plant Pathol. 60, 2–14 (2011).

    Google Scholar 

  10. 10.

    Fisher, M. C. et al. Emerging fungal threats to animal, plant and ecosystem health. Nature 484, 186–194 (2012).

    CAS  Google Scholar 

  11. 11.

    Scholthof, K.-B. G. et al. Top 10 plant viruses in molecular plant pathology. Mol. Plant Pathol. 12, 938–954 (2011).

    CAS  Google Scholar 

  12. 12.

    da Silva, J. G. The State of Food and Agriculture 2016 (Food and Agriculture Organization of the United Nations, 2016).

  13. 13.

    Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).

    Google Scholar 

  14. 14.

    Hatfield, J. L., Gitelson, A. A., Schepers, J. S. & Walthall, C. L. Application of spectral remote sensing for agronomic decisions. Agron. J. 100, S117–S131 (2008).

    CAS  Google Scholar 

  15. 15.

    Padilla, F. M., Gallardo, M., Peña-Fleitas, M. T., de Souza, R. & Thompson, R. B. Proximal optical sensors for nitrogen management of vegetable crops: a review. Sensors 18, 2083 (2018).

    Google Scholar 

  16. 16.

    Li, L., Zhang, Q. & Huang, D. A review of imaging techniques for plant phenotyping. Sensors 14, 20078–20111 (2014).

    Google Scholar 

  17. 17.

    Smith, A. M., Mancini, M. C. & Nie, S. Bioimaging: second window for in vivo imaging. Nat. Nanotechnol. 4, 710–711 (2009).

    CAS  Google Scholar 

  18. 18.

    Wilson, R. H., Nadeau, K. P., Jaworski, F. B., Tromberg, B. J. & Durkin, A. J. Review of short-wave infrared spectroscopy and imaging methods for biological tissue characterization. J. Biomed. Opt. 20, 030901 (2015).

    Google Scholar 

  19. 19.

    Giraldo, J. P. et al. Plant nanobionics approach to augment photosynthesis and biochemical sensing. Nat. Mater. 13, 400–408 (2014).

    CAS  Google Scholar 

  20. 20.

    Giraldo, J. P. et al. A ratiometric sensor using single chirality near-infrared fluorescent carbon nanotubes: application to in vivo monitoring. Small 11, 3973–3984 (2015).

    CAS  Google Scholar 

  21. 21.

    Kwak, S.-Y. et al. Nanosensor technology applied to living plant systems. Annu. Rev. Anal. Chem. 10, 113–140 (2017).

    Google Scholar 

  22. 22.

    Kwak, S.-Y. et al. Chloroplast-selective gene delivery and expression in planta using chitosan-complexed single-walled carbon nanotube carriers. Nat. Nanotechnol. https://doi.org/10.1038/s41565-019-0375-4 (2019).

    CAS  Google Scholar 

  23. 23.

    Demirer, G. S. et al. High aspect ratio nanomaterials enable delivery of functional genetic material without DNA integration in mature plants. Nat. Nanotechnol. https://doi.org/10.1038/s41565-019-0382-5 (2019).

    CAS  Google Scholar 

  24. 24.

    Walia, A., Waadt, R. & Jones, A. M. Genetically encoded biosensors in plants: pathways to discovery. Annu. Rev. Plant Biol. 69, 497–524 (2018).

    CAS  Google Scholar 

  25. 25.

    Okumoto, S., Jones, A. & Frommer, W. B. Quantitative imaging with fluorescent biosensors. Annu. Rev. Plant Biol. 63, 663–706 (2012).

    CAS  Google Scholar 

  26. 26.

    Heikenfeld, J. et al. Wearable sensors: modalities, challenges, and prospects. Lab Chip 18, 217–248 (2018).

    CAS  Google Scholar 

  27. 27.

    Wong, M. H. et al. Nitroaromatic detection and infrared communication from wild-type plants using plant nanobionics. Nat. Mater. 16, 264–272 (2017).

    CAS  Google Scholar 

  28. 28.

    Koman, V. B. et al. Persistent drought monitoring using a microfluidic-printed electro-mechanical sensor of stomata in planta. Lab Chip 17, 4015–4024 (2017).

    CAS  Google Scholar 

  29. 29.

    Li, J., Wu, H., Santana, I., Fahlgren, M. & Giraldo, J. P. Standoff optical glucose sensing in photosynthetic organisms by a quantum dot fluorescent probe. ACS Appl. Mater. Interfaces 10, 28279–28289 (2018).

    CAS  Google Scholar 

  30. 30.

    Lee, K. et al. In-situ synthesis of carbon nanotube-graphite electronic devices and their integrations onto surfaces of live plants and insects. Nano Lett. 14, 2647–2654 (2014).

    CAS  Google Scholar 

  31. 31.

    Conner, A. J., Glare, T. R. & Nap, J.-P. The release of genetically modified crops into the environment. Part II. Overview of ecological risk assessment. Plant J. 33, 19–46 (2003).

    Google Scholar 

  32. 32.

    Davison, J. GM plants: science, politics and EC regulations. Plant Sci. 178, 94–98 (2010).

    CAS  Google Scholar 

  33. 33.

    Griffitt, R. J., Luo, J., Gao, J., Bonzongo, J.-C. & Barber, D. S. Effects of particle composition and species on toxicity of metallic nanomaterials in aquatic organisms. Environ. Toxicol. Chem. 27, 1972–1978 (2008).

    CAS  Google Scholar 

  34. 34.

    Parks, A. N. et al. Bioaccumulation and toxicity of single-walled carbon nanotubes to benthic organisms at the base of the marine food chain. Environ. Toxicol. Chem. 32, 1270–1277 (2013).

    CAS  Google Scholar 

  35. 35.

    Bour, A. et al. Toxicity of CeO2 nanoparticles on a freshwater experimental trophic chain: a study in environmentally relevant conditions through the use of mesocosms. Nanotoxicology 10, 245–255 (2016).

    CAS  Google Scholar 

  36. 36.

    Hong, G., Diao, S., Antaris, A. L. & Dai, H. Carbon nanomaterials for biological imaging and nanomedicinal therapy. Chem. Rev. 115, 10816–10906 (2015).

    CAS  Google Scholar 

  37. 37.

    Oh, E. et al. Meta-analysis of cellular toxicity for cadmium-containing quantum dots. Nat. Nanotechnol. 11, 479–486 (2016).

    CAS  Google Scholar 

  38. 38.

    Havrdova, M. et al. Toxicity of carbon dots – Effect of surface functionalization on the cell viability, reactive oxygen species generation and cell cycle. Carbon 99, 238–248 (2016).

    CAS  Google Scholar 

  39. 39.

    Wang, P., Lombi, E., Zhao, F.-J. & Kopittke, P. M. Nanotechnology: a new opportunity in plant sciences. Trends Plant Sci. 21, 699–712 (2016).

    CAS  Google Scholar 

  40. 40.

    Chaerle, L. & Van Der Straeten, D. Imaging techniques and the early detection of plant stress. Trends Plant Sci. 5, 495–501 (2000).

    CAS  Google Scholar 

  41. 41.

    Humplík, J. F., Lazár, D., Husičková, A. & Spíchal, L. Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses: a review. Plant Methods 11, 29 (2015).

    Google Scholar 

  42. 42.

    Zhao, Y.-R., Li, X., Yu, K.-Q., Cheng, F. & He, Y. Hyperspectral imaging for determining pigment contents in cucumber leaves in response to angular leaf spot disease. Sci. Rep. 6, 27790 (2016).

    CAS  Google Scholar 

  43. 43.

    Valle, B. et al. PYM: a new, affordable, image-based method using a Raspberry Pi to phenotype plant leaf area in a wide diversity of environments. Plant Methods 13, 98 (2017).

    Google Scholar 

  44. 44.

    Zarco-Tejada, P. J., González-Dugo, V. & Berni, J. A. J. Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sens. Environ. 117, 322–337 (2012).

    Google Scholar 

  45. 45.

    Leinonen, I., Grant, O. M., Tagliavia, C. P. P., Chaves, M. M. & Jones, H. G. Estimating stomatal conductance with thermal imagery. Plant Cell Environ. 29, 1508–1518 (2006).

    CAS  Google Scholar 

  46. 46.

    Al-Tamimi, N. et al. Salinity tolerance loci revealed in rice using high-throughput non-invasive phenotyping. Nat. Commun. 7, 13342 (2016).

    Google Scholar 

  47. 47.

    Cohen, Y., Alchanatis, V., Meron, M., Saranga, Y. & Tsipris, J. Estimation of leaf water potential by thermal imagery and spatial analysis. J. Exp. Bot. 56, 1843–1852 (2005).

    CAS  Google Scholar 

  48. 48.

    Munns, R., James, R. A., Sirault, X. R. R., Furbank, R. T. & Jones, H. G. New phenotyping methods for screening wheat and barley for beneficial responses to water deficit. J. Exp. Bot. 61, 3499–3507 (2010).

    CAS  Google Scholar 

  49. 49.

    Sankaran, S., Mishra, A., Ehsani, R. & Davis, C. A review of advanced techniques for detecting plant diseases. Comput. Electron. Agric. 72, 1–13 (2010).

    Google Scholar 

  50. 50.

    Martinelli, F. et al. Advanced methods of plant disease detection. A review. Agron. Sustain. Dev. 35, 1–25 (2015).

    Google Scholar 

  51. 51.

    Mahlein, A.-K. Plant disease detection by imaging sensors - parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis. 100, 241–251 (2016).

    Google Scholar 

  52. 52.

    Grimmer, M. K., John Foulkes, M. & Paveley, N. D. Foliar pathogenesis and plant water relations: a review. J. Exp. Bot. 63, 4321–4331 (2012).

    CAS  Google Scholar 

  53. 53.

    Altangerel, N. et al. In vivo diagnostics of early abiotic plant stress response via Raman spectroscopy. Proc. Natl Acad. Sci. USA 114, 3393–3396 (2017).

    CAS  Google Scholar 

  54. 54.

    Zarco-Tejada, P. J. et al. Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nat. Plants 4, 432–439 (2018).

    CAS  Google Scholar 

  55. 55.

    Gilroy, S. et al. A tidal wave of signals: calcium and ROS at the forefront of rapid systemic signaling. Trends Plant Sci. 19, 623–630 (2014).

    CAS  Google Scholar 

  56. 56.

    Zhu, J.-K. Abiotic stress signaling and responses in plants. Cell 167, 313–324 (2016).

    CAS  Google Scholar 

  57. 57.

    Suzuki, N. et al. Temporal-spatial interaction between reactive oxygen species and abscisic acid regulates rapid systemic acclimation in plants. Plant Cell 25, 3553–3569 (2013).

    CAS  Google Scholar 

  58. 58.

    Mittler, R. ROS are good. Trends Plant Sci. 22, 11–19 (2017).

    CAS  Google Scholar 

  59. 59.

    Kiegle, E., Moore, C. A., Haseloff, J., Tester, M. A. & Knight, M. R. Cell-type-specific calcium responses to drought, salt and cold in the Arabidopsis root. Plant J. 23, 267–278 (2000).

    CAS  Google Scholar 

  60. 60.

    Mittler, R. et al. ROS signaling: the new wave? Trends Plant Sci. 16, 300–309 (2011).

    CAS  Google Scholar 

  61. 61.

    Rolland, F., Baena-Gonzalez, E. & Sheen, J. Sugar sensing and signaling in plants: conserved and novel mechanisms. Annu. Rev. Plant Biol. 57, 675–709 (2006).

    CAS  Google Scholar 

  62. 62.

    Tognetti, J. A., Pontis, H. G. & Martínez-Noël, G. M. A. Sucrose signaling in plants: a world yet to be explored. Plant Signal. Behav. 8, e23316 (2013).

    Google Scholar 

  63. 63.

    Zhu, Q. et al. FRET-based glucose imaging identifies glucose signalling in response to biotic and abiotic stresses in rice roots. J. Plant Physiol. 215, 65–72 (2017).

    CAS  Google Scholar 

  64. 64.

    Kim, T.-H., Böhmer, M., Hu, H., Nishimura, N. & Schroeder, J. I. Guard cell signal transduction network: advances in understanding abscisic acid, CO2, and Ca2+ signaling. Annu. Rev. Plant Biol. 61, 561–591 (2010).

    CAS  Google Scholar 

  65. 65.

    Yoshida, T., Mogami, J. & Yamaguchi-Shinozaki, K. ABA-dependent and ABA-independent signaling in response to osmotic stress in plants. Curr. Opin. Plant Biol. 21, 133–139 (2014).

    CAS  Google Scholar 

  66. 66.

    Delledonne, M., Xia, Y., Dixon, R. A. & Lamb, C. Nitric oxide functions as a signal in plant disease resistance. Nature 394, 585–588 (1998).

    CAS  Google Scholar 

  67. 67.

    Lin, Y. et al. The herbivore-induced plant volatiles methyl salicylate and menthol positively affect growth and pathogenicity of entomopathogenic fungi. Sci. Rep. 7, 40494 (2017).

    CAS  Google Scholar 

  68. 68.

    van Loon, L. C., Geraats, B. P. J. & Linthorst, H. J. M. Ethylene as a modulator of disease resistance in plants. Trends Plant Sci. 11, 184–191 (2006).

    Google Scholar 

  69. 69.

    Howe, G. A., Major, I. T. & Koo, A. J. Modularity in jasmonate signaling for multistress resilience. Annu. Rev. Plant Biol. 69, 387–415 (2018).

    CAS  Google Scholar 

  70. 70.

    Klessig, D. F. et al. Nitric oxide and salicylic acid signaling in plant defense. Proc. Natl Acad. Sci. USA 97, 8849–8855 (2000).

    CAS  Google Scholar 

  71. 71.

    Singsaas, E. L. & Sharkey, T. D. The regulation of isoprene emission responses to rapid leaf temperature fluctuations. Plant Cell Environ. 21, 1181–1188 (1998).

    CAS  Google Scholar 

  72. 72.

    Toyota, M. et al. Glutamate triggers long-distance, calcium-based plant defense signaling. Science 361, 1112–1115 (2018).

    CAS  Google Scholar 

  73. 73.

    Li, H., Wang, P., Weber, J. F. & Gerhards, R. Early identification of herbicide stress in soybean (Glycine max (L.) Merr.) using chlorophyll fluorescence imaging technology. Sensors 18, 21 (2017).

    Google Scholar 

  74. 74.

    Deuschle, K. et al. Rapid metabolism of glucose detected with FRET glucose nanosensors in epidermal cells and intact roots of Arabidopsis RNA-silencing mutants. Plant Cell 18, 2314–2325 (2006).

    CAS  Google Scholar 

  75. 75.

    Chaudhuri, B. et al. Protonophore- and pH-insensitive glucose and sucrose accumulation detected by FRET nanosensors in Arabidopsis root tips. Plant J. 56, 948–962 (2008).

    CAS  Google Scholar 

  76. 76.

    Chaudhuri, B., Hörmann, F. & Frommer, W. B. Dynamic imaging of glucose flux impedance using FRET sensors in wild-type Arabidopsis plants. J. Exp. Bot. 62, 2411–2417 (2011).

    CAS  Google Scholar 

  77. 77.

    Krebs, M. et al. FRET-based genetically encoded sensors allow high-resolution live cell imaging of Ca2+ dynamics: improved vectors for Ca2+ imaging in plants. Plant J. 69, 181–192 (2012).

    CAS  Google Scholar 

  78. 78.

    Loro, G. et al. Chloroplast-specific in vivo Ca2+ imaging using yellow cameleon fluorescent protein sensors reveals organelle-autonomous Ca2+ signatures in the stroma. Plant Physiol. 171, 2317–2330 (2016).

    CAS  Google Scholar 

  79. 79.

    Exposito-Rodriguez, M., Laissue, P. P., Yvon-Durocher, G., Smirnoff, N. & Mullineaux, P. M. Photosynthesis-dependent H2O2 transfer from chloroplasts to nuclei provides a high-light signalling mechanism. Nat. Commun. 8, 49 (2017).

    Google Scholar 

  80. 80.

    Keinath, N. F. et al. Live cell imaging with R-GECO1 sheds light on flg22- and chitin-induced transient [Ca2+]cyt patterns in Arabidopsis. Mol. Plant 8, 1188–1200 (2015).

    CAS  Google Scholar 

  81. 81.

    Nietzel, T. et al. The fluorescent protein sensor roGFP2-Orp1 monitors in vivo H2 O2 and thiol redox integration and elucidates intracellular H2O2 dynamics during elicitor-induced oxidative burst in Arabidopsis. New Phytol. 221, 1649–1664 (2019).

    CAS  Google Scholar 

  82. 82.

    Wong, M. H. et al. Lipid exchange envelope penetration (LEEP) of nanoparticles for plant engineering: a universal localization mechanism. Nano Lett. 16, 1161–1172 (2016).

    CAS  Google Scholar 

  83. 83.

    Yagi, Y. & Shiina, T. Recent advances in the study of chloroplast gene expression and its evolution. Front. Plant Sci. 5, 61 (2014).

    Google Scholar 

  84. 84.

    Yu, Q., Lutz, K. A. & Maliga, P. Efficient plastid transformation in rabidopsis. Plant Physiol. 175, 186–193 (2017).

    CAS  Google Scholar 

  85. 85.

    Shapiguzov, A., Vainonen, J. P., Wrzaczek, M. & Kangasjärvi, J. ROS-talk: how the apoplast, the chloroplast, and the nucleus get the message through. Front. Plant Sci. 3, 292 (2012).

    CAS  Google Scholar 

  86. 86.

    Guo, Z., Park, S., Yoon, J. & Shin, I. Recent progress in the development of near-infrared fluorescent probes for bioimaging applications. Chem. Soc. Rev. 43, 16–29 (2014).

    Google Scholar 

  87. 87.

    Kruss, S. et al. Carbon nanotubes as optical biomedical sensors. Adv. Drug Deliv. Rev. 65, 1933–1950 (2013).

    CAS  Google Scholar 

  88. 88.

    Son, D. et al. Nanoneedle transistor-based sensors for the selective detection of intracellular calcium ions. ACS Nano 5, 3888–3895 (2011).

    CAS  Google Scholar 

  89. 89.

    Zhang, J. et al. Molecular recognition using corona phase complexes made of synthetic polymers adsorbed on carbon nanotubes. Nat. Nanotechnol. 8, 959–968 (2013).

    CAS  Google Scholar 

  90. 90.

    Kruss, S. et al. Neurotransmitter detection using corona phase molecular recognition on fluorescent single-walled carbon nanotube sensors. J. Am. Chem. Soc. 136, 713–724 (2014).

    CAS  Google Scholar 

  91. 91.

    Kruss, S. et al. High-resolution imaging of cellular dopamine efflux using a fluorescent nanosensor array. Proc. Natl Acad. Sci. USA 114, 1789–1794 (2017).

    CAS  Google Scholar 

  92. 92.

    Zrazhevskiy, P., Sena, M. & Gao, X. Designing multifunctional quantum dots for bioimaging, detection, and drug delivery. Chem. Soc. Rev. 39, 4326–4354 (2010).

    CAS  Google Scholar 

  93. 93.

    Hong, S., Lee, M. Y., Jackson, A. O. & Lee, L. P. Bioinspired optical antennas: gold plant viruses. Light Sci. Appl. 4, e267 (2015).

    CAS  Google Scholar 

  94. 94.

    Richardson, J. J. & Liang, K. Nano-biohybrids: in vivo synthesis of metal-organic frameworks inside living plants. Small 14, (2018).

  95. 95.

    Yu, M. K., Park, J. & Jon, S. Targeting strategies for multifunctional nanoparticles in cancer imaging and therapy. Theranostics 2, 3–44 (2012).

    CAS  Google Scholar 

  96. 96.

    Liu, Z., Tabakman, S., Welsher, K. & Dai, H. Carbon nanotubes in biology and medicine: in vitro and in vivo detection, imaging and drug delivery. Nano Res. 2, 85–120 (2009).

    CAS  Google Scholar 

  97. 97.

    Meyer, D., Hagemann, A. & Kruss, S. Kinetic requirements for spatiotemporal chemical imaging with fluorescent nanosensors. ACS Nano 11, 4017–4027 (2017).

    CAS  Google Scholar 

  98. 98.

    Oren, S., Ceylan, H., Schnable, P. S. & Dong, L. High-resolution patterning and transferring of graphene-based nanomaterials onto tape toward roll-to-roll production of tape-based wearable sensors. Adv. Mater. Technol. 2, 1700223 (2017).

    Google Scholar 

  99. 99.

    Esser, B., Schnorr, J. M. & Swager, T. M. Selective detection of ethylene gas using carbon nanotube-based devices: utility in determination of fruit ripeness. Angew. Chem. Int. Ed. Engl. 51, 5752–5756 (2012).

    CAS  Google Scholar 

  100. 100.

    Lee, H. et al. A graphene-based electrochemical device with thermoresponsive microneedles for diabetes monitoring and therapy. Nat. Nanotechnol. 11, 566–572 (2016).

    Google Scholar 

  101. 101.

    Bandodkar, A. J., Jeerapan, I., You, J.-M., Nuñez-Flores, R. & Wang, J. Highly stretchable fully-printed CNT-based electrochemical sensors and biofuel cells: combining intrinsic and design-induced stretchability. Nano Lett. 16, 721–727 (2016).

    CAS  Google Scholar 

  102. 102.

    Kong, J. et al. Nanotube molecular wires as chemical sensors. Science 287, 622–625 (2000).

    CAS  Google Scholar 

  103. 103.

    Cattanach, K., Kulkarni, R. D., Kozlov, M. & Manohar, S. K. Flexible carbon nanotube sensors for nerve agent simulants. Nanotechnology 17, 4123–4128 (2006).

    CAS  Google Scholar 

  104. 104.

    Novak, J. P. et al. Nerve agent detection using networks of single-walled carbon nanotubes. Appl. Phys. Lett. 83, 4026–4028 (2003).

    CAS  Google Scholar 

  105. 105.

    Lee, C. Y., Sharma, R., Radadia, A. D., Masel, R. I. & Strano, M. S. On-chip micro gas chromatograph enabled by a noncovalently functionalized single-walled carbon nanotube sensor array. Angew. Chem. Int. Ed. Engl. 47, 5018–5021 (2008).

    CAS  Google Scholar 

  106. 106.

    Liu, J. et al. Syringe-injectable electronics. Nat. Nanotechnol. 10, 629–636 (2015).

    CAS  Google Scholar 

  107. 107.

    Xie, C. et al. Three-dimensional macroporous nanoelectronic networks as minimally invasive brain probes. Nat. Mater. 14, 1286–1292 (2015).

    CAS  Google Scholar 

  108. 108.

    Tripodi, P., Massa, D., Venezia, A. & Cardi, T. Sensing technologies for precision phenotyping in vegetable crops: current status and future challenges. Agronomy 8, 57 (2018).

    Google Scholar 

  109. 109.

    White, J. W. et al. Field-based phenomics for plant genetics research. Field Crops Res. 133, 101–112 (2012).

    Google Scholar 

  110. 110.

    Lelong, C. C. D. et al. Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors 8, 3557–3585 (2008).

    Google Scholar 

  111. 111.

    Bai, G., Ge, Y., Hussain, W., Baenziger, P. S. & Graef, G. A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding. Comput. Electron. Agric. 128, 181–192 (2016).

    Google Scholar 

  112. 112.

    Gubbi, J., Buyya, R., Marusic, S. & Palaniswami, M. Internet of Things (IoT): A vision, architectural elements, and future directions. Future Gener. Comp. Syst. 29, 1645–1660 (2013).

    Google Scholar 

  113. 113.

    García-Tejero, I. F. et al. Assessing the crop-water status in almond (Prunus dulcis Mill.) trees via thermal imaging camera connected to smartphone. Sensors 18, (2018).

    Google Scholar 

  114. 114.

    Baret, F., Houlès, V. & Guérif, M. Quantification of plant stress using remote sensing observations and crop models: the case of nitrogen management. J. Exp. Bot. 58, 869–880 (2007).

    CAS  Google Scholar 

  115. 115.

    Wolfert, S., Ge, L., Verdouw, C. & Bogaardt, M.-J. Big data in smart farming – A review. Agric. Syst. 153, 69–80 (2017).

    Google Scholar 

  116. 116.

    Iverson, N. M. et al. In vivo biosensing via tissue-localizable near-infrared-fluorescent single-walled carbon nanotubes. Nat. Nanotechnol. 8, 873–880 (2013).

    CAS  Google Scholar 

  117. 117.

    Graham, J. H. et al. Potential of nano-formulated zinc oxide for control of citrus canker on grapefruit trees. Plant Dis. 100, 2442–2447 (2016).

    CAS  Google Scholar 

  118. 118.

    Borgatta, J. et al. Copper based nanomaterials suppress root fungal disease in watermelon (Citrullus lanatus): role of particle morphology, composition and dissolution behavior. ACS Sustain. Chem. Eng. 6, 14847–14856 (2018).

    CAS  Google Scholar 

  119. 119.

    Wu, H., Tito, N. & Giraldo, J. P. Anionic cerium oxide nanoparticles protect plant photosynthesis from abiotic stress by scavenging reactive oxygen species. ACS Nano 11, 11283–11297 (2017).

    CAS  Google Scholar 

  120. 120.

    Wu, H., Shabala, L., Shabala, S. & Giraldo, J. P. Hydroxyl radical scavenging by cerium oxide nanoparticles improves Arabidopsis salinity tolerance by enhancing leaf mesophyll potassium retention. Environ. Sci. Nano 5, 1567–1583 (2018).

    CAS  Google Scholar 

  121. 121.

    Alhamid, J. O. et al. Cellulose nanocrystals reduce cold damage to reproductive buds in fruit crops. Biosyst. Eng. 172, 124–133 (2018).

    Google Scholar 

  122. 122.

    Emmi, L., Gonzalez-de-Soto, M., Pajares, G. & Gonzalez-de-Santos, P. Integrating sensory/actuation systems in agricultural vehicles. Sensors 14, 4014–4049 (2014).

    Google Scholar 

  123. 123.

    Pajares, G. et al. Machine-vision systems selection for agricultural vehicles: a guide. J. Imaging 2, 34 (2016).

    Google Scholar 

  124. 124.

    Ibayashi, H. et al. A reliable wireless control system for tomato hydroponics. Sensors 16, 644 (2016).

    Google Scholar 

  125. 125.

    Torney, F., Trewyn, B. G., Lin, V. S.-Y. & Wang, K. Mesoporous silica nanoparticles deliver DNA and chemicals into plants. Nat. Nanotechnol. 2, 295–300 (2007).

    CAS  Google Scholar 

  126. 126.

    Zhao, X. et al. Pollen magnetofection for genetic modification with magnetic nanoparticles as gene carriers. Nat. Plants 3, 956–964 (2017).

    CAS  Google Scholar 

  127. 127.

    Cheeseman, J. M. Hydrogen peroxide concentrations in leaves under natural conditions. J. Exp. Bot. 57, 2435–2444 (2006).

    CAS  Google Scholar 

  128. 128.

    AbdElgawad, H. et al. High salinity induces different oxidative stress and antioxidant responses in maize seedlings organs. Front. Plant Sci. 7, 276 (2016).

    Google Scholar 

  129. 129.

    Miller, G. et al. The plant NADPH oxidase RBOHD mediates rapid systemic signaling in response to diverse stimuli. Sci. Signal. 2, ra45 (2009).

    Google Scholar 

  130. 130.

    Jin, H. et al. Detection of single-molecule H2O2 signalling from epidermal growth factor receptor using fluorescent single-walled carbon nanotubes. Nat. Nanotechnol. 5, 302–309 (2010).

    CAS  Google Scholar 

  131. 131.

    Yum, K. et al. Boronic acid library for selective, reversible near-infrared fluorescence quenching of surfactant suspended single-walled carbon nanotubes in response to glucose. ACS Nano 6, 819–830 (2012).

    CAS  Google Scholar 

  132. 132.

    Smyth, D. A., Repetto, B. M. & Seidel, N. E. Cultivar differences in soluble sugar content of mature rice grain. Physiol. Plant. 68, 367–374 (1986).

    CAS  Google Scholar 

  133. 133.

    Zhu, J. et al. Characterization of sugar contents and sucrose metabolizing enzymes in developing leaves of Hevea brasiliensis. Front. Plant Sci. 9, 58 (2018).

    Google Scholar 

  134. 134.

    Bush, D. S. Calcium regulation in plant cells and its role in signaling. Annu. Rev. Plant Physiol. Plant Mol. Biol. 46, 95–122 (1995).

    CAS  Google Scholar 

  135. 135.

    Sanders, D., Brownlee, C. & Harper, J. F. Communicating with calcium. Plant Cell 11, 691–706 (1999).

    CAS  Google Scholar 

  136. 136.

    Lecourieux, D., Mazars, C., Pauly, N., Ranjeva, R. & Pugin, A. Analysis and effects of cytosolic free calcium increases in response to elicitors in Nicotiana plumbaginifolia cells. Plant Cell 14, 2627–2641 (2002).

    CAS  Google Scholar 

  137. 137.

    White, P. J. & Broadley, M. R. Calcium in plants. Ann. Bot. 92, 487–511 (2003).

    CAS  Google Scholar 

  138. 138.

    Choi, W.-G., Toyota, M., Kim, S.-H., Hilleary, R. & Gilroy, S. Salt stress-induced Ca2+ waves are associated with rapid, long-distance root-to-shoot signaling in plants. Proc. Natl Acad. Sci. USA 111, 6497–6502 (2014).

    CAS  Google Scholar 

  139. 139.

    Matsuda, T., Horikawa, K., Saito, K. & Nagai, T. Highlighted Ca2+ imaging with a genetically encoded ‘caged’ indicator. Sci. Rep. 3, 1398 (2013).

    Google Scholar 

  140. 140.

    Cho, J.-H. et al. The GCaMP-R family of genetically encoded ratiometric calcium indicators. ACS Chem. Biol. 12, 1066–1074 (2017).

    CAS  Google Scholar 

  141. 141.

    Wu, J. et al. Red fluorescent genetically encoded Ca2+ indicators for use in mitochondria and endoplasmic reticulum. Biochem. J. 464, 13–22 (2014).

    CAS  Google Scholar 

  142. 142.

    Thomas, D. D., Liu, X., Kantrow, S. P. & Lancaster, J. R. The biological lifetime of nitric oxide: Implications for the perivascular dynamics of NO and O2. Proc. Natl Acad. Sci. USA 98, 355–360 (2001).

    CAS  Google Scholar 

  143. 143.

    Zhang, J. et al. Single molecule detection of nitric oxide enabled by d(AT) 15 DNA adsorbed to near infrared fluorescent single-walled carbon nanotubes. J. Am. Chem. Soc. 133, 567–581 (2011).

    CAS  Google Scholar 

  144. 144.

    Knoester, M. et al. Ethylene-insensitive tobacco lacks nonhost resistance against soil-borne fungi. Proc. Natl Acad. Sci. USA 95, 1933–1937 (1998).

    CAS  Google Scholar 

  145. 145.

    Knoester, M., Pieterse, C. M., Bol, J. F. & Van Loon, L. C. Systemic resistance in Arabidopsis induced by rhizobacteria requires ethylene-dependent signaling at the site of application. Mol. Plant. Microbe Interact. 12, 720–727 (1999).

    CAS  Google Scholar 

  146. 146.

    McMillan, G. R., Calvert, J. G. & Pitts, J. N. Detection and lifetime of enol-acetone in the photolysis of 2-pentanone vapor. J. Am. Chem. Soc. 86, 3602–3605 (1964).

    CAS  Google Scholar 

  147. 147.

    Du, H., Liu, H. & Xiong, L. Endogenous auxin and jasmonic acid levels are differentially modulated by abiotic stresses in rice. Front. Plant Sci. 4, 397 (2013).

    Google Scholar 

  148. 148.

    Larrieu, A. et al. A fluorescent hormone biosensor reveals the dynamics of jasmonate signalling in plants. Nat. Commun. 6, 6043 (2015).

    CAS  Google Scholar 

  149. 149.

    Li, Y. et al. A reagent-assisted method in SERS detection of methyl salicylate. Spectrochim. Acta A Mol. Biomol. Spectrosc. 195, 172–175 (2018).

    CAS  Google Scholar 

  150. 150.

    Verslues, P. E. & Bray, E. A. Role of abscisic acid (ABA) and Arabidopsis thaliana ABA-insensitive loci in low water potential-induced ABA and proline accumulation. J. Exp. Bot. 57, 201–212 (2006).

    CAS  Google Scholar 

  151. 151.

    Niu, M. et al. An early ABA-induced stomatal closure, Na+ sequestration in leaf vein and K+ retention in mesophyll confer salt tissue tolerance in Cucurbita species. J. Exp. Bot. 69, 4945–4960 (2018).

    CAS  Google Scholar 

  152. 152.

    Waadt, R. et al. FRET-based reporters for the direct visualization of abscisic acid concentration changes and distribution in Arabidopsis. eLife 3, e01739 (2014).

    Google Scholar 

  153. 153.

    Jones, A. M. et al. Abscisic acid dynamics in roots detected with genetically encoded FRET sensors. eLife 3, e01741 (2014).

    Google Scholar 

  154. 154.

    Shen, J. et al. Organelle pH in the Arabidopsis endomembrane system. Mol. Plant 6, 1419–1437 (2013).

    CAS  Google Scholar 

  155. 155.

    Monshausen, G. B., Bibikova, T. N., Messerli, M. A., Shi, C. & Gilroy, S. Oscillations in extracellular pH and reactive oxygen species modulate tip growth of Arabidopsis root hairs. Proc. Natl Acad. Sci. USA 104, 20996–21001 (2007).

    CAS  Google Scholar 

  156. 156.

    Fasano, J. M. et al. Changes in root cap pH are required for the gravity response of the Arabidopsis root. Plant Cell 13, 907–921 (2001).

    CAS  Google Scholar 

Download references

Acknowledgements

This work was funded by the National Science Foundation under grant no. 1817363 to J.P.G. Funding by the Volkswagen Foundation is acknowledged by S.K.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Juan Pablo Giraldo.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information: Nature Nanotechnology thanks Jorge Gardea-Torresdey and other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Giraldo, J.P., Wu, H., Newkirk, G.M. et al. Nanobiotechnology approaches for engineering smart plant sensors. Nat. Nanotechnol. 14, 541–553 (2019). https://doi.org/10.1038/s41565-019-0470-6

Download citation

Further reading

Search

Quick links

Find nanotechnology articles, nanomaterial data and patents all in one place. Visit Nano by Nature Research