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.

Nanotechnology and artificial intelligence to enable sustainable and precision agriculture

Abstract

Climate change, increasing populations, competing demands on land for production of biofuels and declining soil quality are challenging global food security. Finding sustainable solutions requires bold new approaches and integration of knowledge from diverse fields, such as materials science and informatics. The convergence of precision agriculture, in which farmers respond in real time to changes in crop growth with nanotechnology and artificial intelligence, offers exciting opportunities for sustainable food production. Coupling existing models for nutrient cycling and crop productivity with nanoinformatics approaches to optimize targeting, uptake, delivery, nutrient capture and long-term impacts on soil microbial communities will enable design of nanoscale agrochemicals that combine optimal safety and functionality profiles.

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: Applications of nanotechnology in agriculture, focusing specifically on crop production.
Fig. 2: Applications of machine learning to nano-enabled agriculture.
Fig. 3: The complexity of nanomaterial behaviour in the soil–plant environment and the potential impacts in soil–plant systems.
Fig. 4: Approach to integration of AI models needed to assess nanomaterials behaviour, fate and impact in agriculture based on the interplay between nanomaterial and environmental factors including the crop type and soil characteristics.

References

  1. 1.

    Shahzad, A. N., Qureshi, M. K., Wakeel, A. & Misselbrook, T. Crop production in Pakistan and low nitrogen use efficiencies. Nat. Sustain. 2, 1106–1114 (2019).

    Article  Google Scholar 

  2. 2.

    Kah, M., Tufenkji, N. & White, J. C. Nano-enabled strategies to enhance crop nutrition and protection. Nat. Nanotechnol. 14, 532–540 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  3. 3.

    Lowry, G. V., Avellan, A. & Gilbertson, L. M. Opportunities and challenges for nanotechnology in the agri-tech revolution. Nat. Nanotechnol. 14, 517–522 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  4. 4.

    Giraldo, J. P., Wu, H., Newkirk, G. M. & Kruss, S. Nanobiotechnology approaches for engineering smart plant sensors. Nat. Nanotechnol. 14, 541–553 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  5. 5.

    Kottegoda, N. et al. Urea–hydroxyapatite nanohybrids for slow release of nitrogen. ACS Nano 11, 1214–1221 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Kabiri, S. et al. Graphene oxide: a new carrier for slow release of plant micronutrients. ACS Appl. Mat. Int. 9, 43325–43335 (2017).

    CAS  Article  Google Scholar 

  7. 7.

    Huang, B. et al. Advances in targeted pesticides with environmentally responsive controlled release by nanotechnology. Nanomaterials 8, 102 (2018).

    PubMed Central  Article  CAS  Google Scholar 

  8. 8.

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

    CAS  Article  Google Scholar 

  9. 9.

    Simonin, M. et al. Titanium dioxide nanoparticles strongly impact soil microbial function by affecting archaeal nitrifiers. Sci. Rep. 6, 33643 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Grün, A.-L. et al. Impact of silver nanoparticles (AgNP) on soil microbial community depending on functionalization, concentration, exposure time, and soil texture. Environ. Sci. Eur. 31, 15 (2019).

    Article  CAS  Google Scholar 

  11. 11.

    Hofmann, T. et al. Technology readiness and overcoming barriers to sustainably implement nanotechnology-enabled plant agriculture. Nat. Food 1, 416–425 (2020).

    Article  Google Scholar 

  12. 12.

    Stone, D., Harper, B. J., Lynch, I., Dawson, K. & Harper, S. L. Exposure assessment: recommendations for nanotechnology-based pesticides. Int. J. Occup. Environ. Health 16, 467–474 (2010).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  13. 13.

    Kookana, R. S. et al. Nanopesticides: guiding principles for regulatory evaluation of environmental risks. J. Agric. Food Chem. 62, 4227–4240 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  14. 14.

    Zhang, P. et al. Nanomaterial transformation in the soil–plant system: implications for food safety and application in agriculture. Small 16, 2000705 (2020).

    CAS  Article  Google Scholar 

  15. 15.

    Lombi, E., Donner, E., Dusinska, M. & Wickson, F. A. One health approach to managing the applications and implications of nanotechnologies in agriculture. Nat. Nanotechnol. 14, 523–531 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  16. 16.

    Mouchlis, V. D. et al. Advances in de novo drug design: from conventional to machine learning methods. Int. J. Mol. Sci. 22, 1676 (2021).

    PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Nicolaou, C. A., Brown, N. & Pattichis, C. S. Molecular optimization using computational multi-objective methods. Curr. Opin. Drug Discov. Devel. 10, 316–324 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Burello, E. & Worth, A. P. A theoretical framework for predicting the oxidative stress potential of oxide nanoparticles. Nanotoxicology 5, 228–235 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  19. 19.

    Winkler, D. A. Role of artificial intelligence and machine learning in nanosafety. Small 16, 2001883 (2020).

    CAS  Article  Google Scholar 

  20. 20.

    Karatzas, P. et al. Development of deep learning models for predicting the effects of exposure to engineered nanomaterials on Daphnia magna. Small 16, 2001080 (2020).

    CAS  Article  Google Scholar 

  21. 21.

    Heermann, D. F., Duke, H. R. & Buchleiter, G. W. ‘User friendly’ software for an integrated water-energy management system for center pivot irrigation. Comput. Electron. Agric. 1, 41–57 (1985).

    Article  Google Scholar 

  22. 22.

    White, J. W. & Hamilton, J. H. Irradiance and plant temperature monitor/controller. Comput. Electron. Agric. 1, 95–103 (1985).

    Article  Google Scholar 

  23. 23.

    Chlingaryan, A., Sukkarieh, S. & Whelan, B. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: a review. Comput. Electron. Agric. 151, 61–69 (2018).

    Article  Google Scholar 

  24. 24.

    Jones, J. W. et al. Toward a new generation of agricultural system data, models, and knowledge products: state of agricultural systems science. Agric. Sys. 155, 269–288 (2017).

    Article  Google Scholar 

  25. 25.

    Gumière, S. J. et al. Machine learning vs. physics-based modeling for real-time irrigation management. Front. Water 2, 8 (2020).

    Article  Google Scholar 

  26. 26.

    Klein Goldewijk, K., Dekker, S. C. & van Zanden, J. L. Per-capita estimations of long-term historical land use and the consequences for global change research. J. Land Use Sci. 12, 313–337 (2017).

    Google Scholar 

  27. 27.

    Lassaletta, L., Billen, G., Grizzetti, B., Anglade, J. & Garnier, J. 50 year trends in nitrogen use efficiency of world cropping systems: the relationship between yield and nitrogen input to cropland. Environ. Res. Lett. 9, 105011 (2014).

    Article  Google Scholar 

  28. 28.

    van Grinsven, H. J. et al. Losses of ammonia and nitrate from agriculture and their effect on nitrogen recovery in the European Union and the United States between 1900 and 2050. J. Environ. Qual. 44, 356–367 (2015).

    PubMed  Article  CAS  Google Scholar 

  29. 29.

    Burney, J. A., Davis, S. J. & Lobell, D. B. Greenhouse gas mitigation by agricultural intensification. Proc. Natl Acad. Sci. USA 107, 12052–12057 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Rockström, J. et al. Planetary boundaries: exploring the safe operating space for humanity. Ecol. Soc. 14, 32 (2009).

    Article  Google Scholar 

  31. 31.

    Raza, S. et al. Piling up reactive nitrogen and declining nitrogen use efficiency in Pakistan: a challenge not challenged (1961–2013). Environ. Res. Lett. 13, 034012 (2018).

    Article  Google Scholar 

  32. 32.

    Schütz, L. et al. Improving crop yield and nutrient use efficiency via biofertilization—a global meta-analysis. Front. Plant Sci. 8, 2204 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Sharma, L. K. & Bali, S. K. A review of methods to improve nitrogen use efficiency in agriculture. Sustainability 10, 51 (2018).

    Article  CAS  Google Scholar 

  34. 34.

    Oerke, E.-C. Crop losses to pests. J. Agric. Sci. 144, 31–43 (2006).

    Article  Google Scholar 

  35. 35.

    Bindraban, P. S., Dimkpa, C., Nagarajan, L., Roy, A. & Rabbinge, R. Revisiting fertilisers and fertilisation strategies for improved nutrient uptake by plants. Biol. Fertil. Soils 51, 897–911 (2015).

    CAS  Article  Google Scholar 

  36. 36.

    Aktar, W., Sengupta, D. & Chowdhury, A. Impact of pesticides use in agriculture: their benefits and hazards. Interdiscip. Toxicol. 2, 1–12 (2009).

    PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    National Academies of Sciences, Engineering, and Medicine. Science Breakthroughs to Advance Food and Agricultural Research by 2030 (National Academies Press, 2019).

  38. 38.

    Parry, M. L. Climate Change and World Agriculture (Routledge, 2019).

  39. 39.

    Tian, H., Kah, M. & Kariman, K. Are nanoparticles a threat to mycorrhizal and rhizobial symbioses? A critical review. Front. Microbiol. 10, 1660 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Eymard-Vernain, E. et al. Impact of a model soil microorganism and of its secretome on the fate of silver nanoparticles. Environ. Sci. Technol. 52, 71–78 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  41. 41.

    Xu, X., Bai, B., Wang, H. & Suo, Y. A near-infrared and temperature-responsive pesticide release platform through core–shell polydopamine@ PNIPAm nanocomposites. ACS Appl. Mat. Int. 9, 6424–6432 (2017).

    CAS  Article  Google Scholar 

  42. 42.

    Xu, L. et al. The crucial role of environmental coronas in determining the biological effects of engineered nanomaterials. Small 16, 2003691 (2020).

    CAS  Article  Google Scholar 

  43. 43.

    Svendsen, C. et al. Key principles and operational practices for improved nanotechnology environmental exposure assessment. Nat. Nanotechnol. 15, 731–742 (2020).

    CAS  PubMed  Article  Google Scholar 

  44. 44.

    Cohen, Y., Rallo, R., Liu, R. & Liu, H. H. In silico analysis of nanomaterials hazard and risk. Acc. Chem. Res. 46, 802–812 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  45. 45.

    Lamon, L. et al. Grouping of nanomaterials to read-across hazard endpoints: from data collection to assessment of the grouping hypothesis by application of chemoinformatic techniques. Part. Fibre Toxicol. 15, 37 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Varsou, D.-D. et al. A safe-by-design tool for functionalised nanomaterials through the Enalos Nanoinformatics cloud platform. Nanoscale Adv. 1, 706–718 (2019).

    Article  Google Scholar 

  47. 47.

    Findlay, M. R., Freitas, D. N., Mobed-Miremadi, M. & Wheeler, K. E. Machine learning provides predictive analysis into silver nanoparticle protein corona formation from physicochemical properties. Environ. Sci. Technol. 5, 64–71 (2018).

    CAS  Google Scholar 

  48. 48.

    Duan, Y. et al. Prediction of protein corona on nanomaterials by machine learning using novel descriptors. Small 17, 100207 (2020).

    Google Scholar 

  49. 49.

    Ban, Z. et al. Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles. Proc. Natl Acad. Sci.USA 117, 10492–10499 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Afantitis, A., Melagraki, G., Tsoumanis, A., Valsami-Jones, E. & Lynch, I. Nanoinformatics decision support tool for the virtual screening of gold nanoparticle cellular association using protein corona fingerprints. Nanotoxicology 12, 1148–1165 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  51. 51.

    Money, E. S., Reckhow, K. H. & Wiesner, M. R. The use of Bayesian networks for nanoparticle risk forecasting: model formulation and baseline evaluation. Sci. Total Environ. 426, 436–445 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  52. 52.

    Money, E. S., Barton, L. E., Dawson, J., Reckhow, K. H. & Wiesner, M. R. Validation and sensitivity of the FINE Bayesian network for forecasting aquatic exposure to nano-silver. Sci. Total Environ. 473, 685–691 (2014).

    PubMed  Article  CAS  Google Scholar 

  53. 53.

    Murphy, F. et al. A tractable method for measuring nanomaterial risk using Bayesian networks. Nanoscale Res. Lett. 11, 503 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Gerloff, K. et al. The adverse outcome pathway approach in nanotoxicology. J. Comput. Toxcol. 1, 3–11 (2017).

    Article  Google Scholar 

  55. 55.

    Jeong, J. et al. Developing adverse outcome pathways on silver nanoparticle-induced reproductive toxicity via oxidative stress in the nematode Caenorhabditis elegans using a Bayesian network model. Nanotoxicology 12, 1182–1197 (2018).

    CAS  PubMed  Article  Google Scholar 

  56. 56.

    Afantitis, A., Tsoumanis, A. & Melagraki, G. J. C. M. C. Enalos suite of tools: enhancing cheminformatics and nanoinformatics through KNIME. Curr. Med. Chem. 27, 6523–6535 (2020).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  57. 57.

    Gajewicz, A. What if the number of nanotoxicity data is too small for developing predictive nano-QSAR models? An alternative read-across based approach for filling data gaps. Nanoscale 9, 8435–8448 (2017).

    CAS  PubMed  Article  Google Scholar 

  58. 58.

    Lee, B. et al. Statistical characterization of the morphologies of nanoparticles through machine learning based electron microscopy image analysis. ACS Nano 14, 17125–17133 (2020).

    CAS  Article  Google Scholar 

  59. 59.

    Varsou, D. D. et al. Zeta‐potential read‐across model utilizing nanodescriptors extracted via the nanoxtract image analysis tool available on the enalos nanoinformatics cloud platform. Small 16, 1906588 (2020).

    CAS  Article  Google Scholar 

  60. 60.

    Papadiamantis, A. G. et al. Predicting cytotoxicity of metal oxide nanoparticles using Isalos Analytics platform. Nanomaterials 10, 2017 (2020).

    CAS  PubMed Central  Article  Google Scholar 

  61. 61.

    Pan, Y. et al. Nano-QSAR modeling for predicting the cytotoxicity of metal oxide nanoparticles using novel descriptors. RSC Adv. 6, 25766–25775 (2016).

    Article  CAS  Google Scholar 

  62. 62.

    Bora, T. et al. Modeling nanomaterial physical properties: theory and simulation. Int. J. Smart Nano Mat. 10, 116–143 (2018).

    Article  Google Scholar 

  63. 63.

    Afantitis, A. et al. NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment. Comput. Struct. Biotechnol. J. 18, 583–602 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. 64.

    Winkler, D. A. Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterials. Toxicol. Appl. Pharmacol. 299, 96–100 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  65. 65.

    McManus, P. et al. Rhizosphere interactions between copper oxide nanoparticles and wheat root exudates in a sand matrix: influences on copper bioavailability and uptake. Environ. Toxicol. Chem. 37, 2619–2632 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  66. 66.

    Zhang, P. et al. Plant species-dependent transformation and translocation of ceria nanoparticles. Environ. Sci. Nano 6, 60–67 (2019).

    CAS  Article  Google Scholar 

  67. 67.

    De Willigen, P. & Neeteson, J. Comparison of six simulation models for the nitrogen cycle in the soil. Fert. Res. 8, 157–171 (1985).

    Article  Google Scholar 

  68. 68.

    Pathak, H. et al. Modelling the quantitative evaluation of soil nutrient supply, nutrient use efficiency, and fertilizer requirements of wheat in India. Nutr. Cycl. Agroecosys. 65, 105–113 (2003).

    CAS  Article  Google Scholar 

  69. 69.

    Janssen, B. H. Simple models and concepts as tools for the study of sustained soil productivity in long-term experiments. II. Crop nutrient equivalents, balanced supplies of available nutrients, and NPK triangles. Plant Soil 339, 17–33 (2011).

    CAS  Article  Google Scholar 

  70. 70.

    Furxhi, I. et al. Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics. Nanotoxicology 13, 827–848 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  71. 71.

    Detailed Review Paper on Amphibian Metamorphosis Assay for the Detection of Thyroid Active Substances OECD Series on Testing and Assessment No. 46 (OECD, 2004).

  72. 72.

    Kar, S., Roy, K., Leszczynski, J. in Advances in QSAR Modeling (ed. Roy, K.) 203–302 (Springer, 2017).

  73. 73.

    Tari, F. A Bayesian network for predicting yield response of winter wheat to fungicide programmes. Comput. Electron Agric. 15, 111–121 (1996).

    Article  Google Scholar 

  74. 74.

    Krouk, G., Lingeman, J., Colon, A. M., Coruzzi, G. & Shasha, D. Gene regulatory networks in plants: learning causality from time and perturbation. Genome Biol. 14, 123 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  75. 75.

    Ohyanagi, H. et al. Plant Omics Data Center: an integrated web repository for interspecies gene expression networks with NLP-based curation. Plant Cell Physiol. 56, e9 (2015).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  76. 76.

    Lum, G., Meinken, J., Orr, J., Frazier, S. & Min, X. J. PlantSecKB: the plant secretome and subcellular proteome knowledgebase. Comput. Mol. Biol. 4, 1–17 (2014).

    Google Scholar 

  77. 77.

    Maggi, F., Tang, F. H., la Cecilia, D. & McBratney, A. PEST-CHEMGRIDS, global gridded maps of the top 20 crop-specific pesticide application rates from 2015 to 2025. Sci. Data 6, 170 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  78. 78.

    Liakos, K. G., Busato, P., Moshou, D., Pearson, S. & Bochtis, D. Machine learning in agriculture: a review. Sensors 18, 2674 (2018).

    Article  Google Scholar 

  79. 79.

    Ha, M. K. et al. Toxicity classification of oxide nanomaterials: effects of data gap filling and PChem score-based screening approaches. Sci. Rep. 8, 3141 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  80. 80.

    Liang, S. et al. Modeling crop yield and nitrogen use efficiency in wheat and maize production systems under future climate change. Nutr. Cycl. Agro. 115, 117–136 (2019).

    CAS  Article  Google Scholar 

  81. 81.

    Liu, Y. et al. Modelling field scale spatial variation in water run-off, soil moisture, N2O emissions and herbage biomass of a grazed pasture using the SPACSYS model. Geoderma 315, 49–58 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  82. 82.

    Sundaramoorthi, D., Dong, L. Machine-learning-based simulation for estimating parameters in portfolio optimization: empirical application to soybean variety selection. SSRN https://doi.org/10.2139/ssrn.3412648 (2019).

  83. 83.

    Vinuesa, R. et al. The role of artificial intelligence in achieving the Sustainable Development Goals. Nat. Comm. 11, 233 (2020).

    CAS  Article  Google Scholar 

  84. 84.

    Afantitis, A., Melagraki, G., Tsoumanis, A., Valsami-Jones, E. & Lynch, I. A nanoinformatics decision support tool for the virtual screening of gold nanoparticle cellular association using protein corona fingerprints. Nanotoxicology 12, 1148–1165 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  85. 85.

    Nendza, M., Dittrich, B., Wenzel, A. & Klein, W. Predictive QSAR models for estimating ecotoxic hazard of plant-protecting agents: target and non-target toxicity. Sci. Total Environ. 109, 527–535 (1991).

    PubMed  Article  PubMed Central  Google Scholar 

  86. 86.

    Kaddi, C. D., Phan, J. H. & Wang, M. D. Computational nanomedicine: modeling of nanoparticle-mediated hyperthermal cancer therapy. Nanomedicine 8, 1323–1333 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  87. 87.

    Kumar, P., Khan, R. A., Choonara, Y. E. & Pillay, V. A prospective overview of the essential requirements in molecular modeling for nanomedicine design. Future Med. Chem. 5, 929–946 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  88. 88.

    Yang, Y. et al. Deep learning for in vitro prediction of pharmaceutical formulations. Acta Pharm. Sin. B 9, 177–185 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  89. 89.

    Papadiamantis, A. G. et al. Metadata stewardship in nanosafety research: community-driven organisation of metadata schemas to support FAIR nanoscience data. Nanomaterials 10, 2033 (2020).

    CAS  PubMed Central  Article  Google Scholar 

  90. 90.

    Martinez, D. S. T. et al. Effect of the albumin corona on the toxicity of combined graphene oxide and cadmium to Daphnia magna and integration of the datasets into the nanocommons knowledge base. Nanomaterials 10, 1936 (2020).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  91. 91.

    Hardy, A. et al. Guidance on risk assessment of the application of nanoscience and nanotechnologies in the food and feed chain: part 1, human and animal health. EFSA J. 16, 5327 (2018).

    Google Scholar 

  92. 92.

    Alsharif, S. A., Power, D., Rouse, I. & Lobaskin, V. In silico prediction of protein adsorption energy on titanium dioxide and gold nanoparticles. Nanomaterials 10, 1967 (2020).

    CAS  PubMed Central  Article  Google Scholar 

  93. 93.

    Hendren, C. O., Lowry, G. V., Unrine, J. M. & Wiesner, M. R. A functional assay-based strategy for nanomaterial risk forecasting. Sci. Total Envrion. 536, 1029–1037 (2015).

    CAS  Article  Google Scholar 

  94. 94.

    Turner, A. A., Rogers, N. M., Geitner, N. K. & Wiesner, M. R. Nanoparticle affinity for natural soils: a functional assay for determining particle attachment efficiency in complex systems. Environ. Sci. Nano 7, 1719–1729 (2020).

    CAS  Article  Google Scholar 

  95. 95.

    Zhao, L. et al. CeO2 and ZnO nanoparticles change the nutritional qualities of cucumber (Cucumis sativus). J. Agric. Food Chem. 62, 2752–2759 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  96. 96.

    Wang, Q., Ma, X., Zhang, W., Pei, H. & Chen, Y. The impact of cerium oxide nanoparticles on tomato (Solanum lycopersicum L.) and its implications for food safety. Metallomics 4, 1105–1112 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  97. 97.

    Tan, W. et al. Effects of the exposure of TiO2 nanoparticles on basil (Ocimum basilicum) for two generations. Sci. Total Environ. 636, 240–248 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  98. 98.

    Hu, X. et al. Graphene oxide amplifies the phytotoxicity of arsenic in wheat. Sci. Rep. 4, 6122 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  99. 99.

    De La Torre-Roche, R. et al. Multiwalled carbon nanotubes and C60 fullerenes differentially impact the accumulation of weathered pesticides in four agricultural plants. Environ. Sci. Technol. 47, 12539–12547 (2013).

    Article  CAS  Google Scholar 

  100. 100.

    Reinsch, B. et al. Sulfidation of silver nanoparticles decreases Escherichia coli growth inhibition. Environ. Sci. Technol. 46, 6992–7000 (2012).

    CAS  PubMed  Article  Google Scholar 

  101. 101.

    Hou, W.-C. et al. Photochemical transformation of graphene oxide in sunlight. Environ. Sci. Technol. 49, 3435–3443 (2015).

    CAS  PubMed  Article  Google Scholar 

  102. 102.

    Dale, A. L. et al. Modeling nanomaterial environmental fate in aquatic systems. Environ. Sci. Technol. 49, 2587–2593 (2015).

    CAS  PubMed  Article  Google Scholar 

  103. 103.

    Silva, V. et al. Pesticide residues in European agricultural soils—a hidden reality unfolded. Sci. Total Environ. 653, 1532–1545 (2019).

    CAS  PubMed  Article  Google Scholar 

  104. 104.

    Baştanlar, Y., Özuysal, M. in miRNomics: MicroRNA Biology and Computational Analysis (eds Yousef, M. & Allmer, J.) 105–128 (Springer, 2014).

  105. 105.

    Nemes, A., Roberts, R. T., Rawls, W. J., Pachepsky, Y. A. & Van Genuchten, M. T. Software to estimate −33 and −1500 kPa soil water retention using the non-parametric k-nearest neighbor technique. Environ. Model. Softw. 23, 254–255 (2008).

    Article  Google Scholar 

  106. 106.

    Nemes, A., Rawls, W. J. & Pachepsky, Y. A. Use of the nonparametric nearest neighbor approach to estimate soil hydraulic properties. Soil Sci. Soc. Am. J. 70, 327–336 (2006).

    CAS  Article  Google Scholar 

  107. 107.

    Pedroso, M., Taylor, J., Tisseyre, B., Charnomordic, B. & Guillaume, S. A segmentation algorithm for the delineation of agricultural management zones. Comput. Electron Agric. 70, 199–208 (2010).

    Article  Google Scholar 

  108. 108.

    Bi, X. et al. Quantitative resolution of nanoparticle sizes using single particle inductively coupled plasma mass spectrometry with the k-means clustering algorithm. J. Anal. Spectrom. 29, 1630–1639 (2014).

    CAS  Article  Google Scholar 

  109. 109.

    Bu, F. & Wang, X. A smart agriculture IoT system based on deep reinforcement learning. Future Gen. Comput. Sys. 99, 500–507 (2019).

    Article  Google Scholar 

  110. 110.

    Sun, B. & Barnard, A. S. Visualising multi-dimensional structure/property relationships with machine learning. J. Phys. Mat. 2, 034003 (2019).

    CAS  Article  Google Scholar 

  111. 111.

    Lamon, L., Aschberger, K., Asturiol, D., Richarz, A. & Worth, A. Grouping of nanomaterials to read-across hazard endpoints: a review. Nanotoxicology 13, 100–118 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

Download references

Acknowledgements

Z.G., I.L. and A.A. acknowledge funding from the EU H2020 project NanoSolveIT (Grant Agreement 814572). I.L. and A.A. acknowledge funding from the EU H2020 projects RiskGone (Grant Agreement 814425) and NanoCommons (Grant Agreement 731032). I.L., P.Z. and Z.G. acknowledge support from the University of Birmingham Institute for Global Innovation Environmental Pollution Solutions theme. S.U. acknowledges funding from the BBSRC Sustainable Agriculture Research and Innovation Club project (BB/R021716/1) and NERC-NSF grant-DiRTS (NE/T012323/1).

Author information

Affiliations

Authors

Contributions

P.Z. and I.L. outlined the manuscript. P.Z., Z.G., S.U. and I.L. wrote the manuscript with contributions and inputs from all authors. P.Z., A.A. and G.M. produced the graphics.

Corresponding author

Correspondence to Peng Zhang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Plants thanks the anonymous reviewers 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

Zhang, P., Guo, Z., Ullah, S. et al. Nanotechnology and artificial intelligence to enable sustainable and precision agriculture. Nat. Plants 7, 864–876 (2021). https://doi.org/10.1038/s41477-021-00946-6

Download citation

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing