Review Article | Published:

Nanobiotechnology approaches for engineering smart plant sensors


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 optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

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

  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).

  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).

  4. 4.

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

  5. 5.

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

  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).

  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).

  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).

  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).

  10. 10.

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

  11. 11.

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

  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).

  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).

  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).

  16. 16.

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

  17. 17.

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

  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).

  19. 19.

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

  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).

  21. 21.

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

  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. (2019).

  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. (2019).

  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).

  25. 25.

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

  26. 26.

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

  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).

  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).

  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).

  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).

  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).

  32. 32.

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

  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).

  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).

  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).

  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).

  37. 37.

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

  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).

  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).

  40. 40.

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

  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).

  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).

  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).

  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).

  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).

  46. 46.

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

  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).

  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).

  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).

  50. 50.

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

  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).

  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).

  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).

  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).

  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).

  56. 56.

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

  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).

  58. 58.

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

  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).

  60. 60.

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

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  72. 72.

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

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  83. 83.

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

  84. 84.

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

  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).

  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).

  87. 87.

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

  88. 88.

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

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  97. 97.

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

  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).

  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).

  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).

  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).

  102. 102.

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

  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).

  104. 104.

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

  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).

  106. 106.

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

  107. 107.

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

  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).

  109. 109.

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

  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).

  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).

  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).

  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).

  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).

  115. 115.

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

  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).

  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).

  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).

  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).

  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).

  121. 121.

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

  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).

  123. 123.

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

  124. 124.

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

  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).

  126. 126.

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

  127. 127.

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

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  135. 135.

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

  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).

  137. 137.

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

  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).

  139. 139.

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

  140. 140.

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

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  148. 148.

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

  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).

  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).

  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).

  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).

  153. 153.

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

  154. 154.

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

  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).

  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).

Download references


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

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

Further reading

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.