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Leveraging edge artificial intelligence for sustainable agriculture

Nature Sustainability volume 7, pages 846–854 (2024)Cite this article

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Abstract

Effectively feeding a burgeoning world population is one of the main goals of sustainable agricultural practices. Digital technology, such as edge artificial intelligence (AI), has the potential to introduce substantial benefits to agriculture by enhancing farming practices that can improve agricultural production efficiency, yield, quality and safety. However, the adoption of edge AI faces several challenges, including the need for innovative and efficient edge AI solutions and greater investment in infrastructure and training, all compounded by various environmental, social and economic constraints. Here we provide a roadmap for leveraging edge AI at the intersection of food production and sustainability.

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Fig. 1: The agrifood supply chain and applications of AI.
Fig. 2: History of AI deployment.
Fig. 3: The energy efficiencies (operations per watt) of state-of-the-art computing chips used for AI/DL applications.
Fig. 4: Edge AI presents a range of opportunities, challenges and implications for sustainable agriculture.

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References

  1. 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).

    Article  CAS  Google Scholar 

  2. The State of Food and Agriculture 2019. Moving Forward on Food Loss and Waste Reduction (FAO, 2019).

  3. Chhogyel, N. & Kumar, L. Climate change and potential impacts on agriculture in Bhutan: a discussion of pertinent issues. Agric. Food Secur. 7, 79 (2018).

    Article  Google Scholar 

  4. Davis, K. F. et al. Tropical forest loss enhanced by large-scale land acquisitions. Nat. Geosci. 13, 482–488 (2020).

    Article  CAS  Google Scholar 

  5. Muluneh, M. G. Impact of climate change on biodiversity and food security: a global perspective—a review article. Agric. Food Secur. 10, 36 (2021).

    Article  Google Scholar 

  6. What is agrobiodiversity? FAO https://www.fao.org/fileadmin/templates/soilbiodiversity/Downloadable_files/agrobiodivesity.pdf (2004).

  7. Byerlee, D., Stevenson, J. & Villoria, N. Does intensification slow crop land expansion or encourage deforestation? Glob. Food Secur. 3, 92–98 (2014).

    Article  Google Scholar 

  8. UN-Water Blueprint for Acceleration: Sustainable Development Goal 6 Synthesis Report on Water and Sanitation 2023 (United Nations Publications, 2023).

  9. Rosa, L. et al. Energy implications of the 21st century agrarian transition. Nat. Commun. 12, 2319 (2021).

    Article  CAS  Google Scholar 

  10. Giller, K. E. et al. The future of farming: who will produce our food? Food Secur. 13, 1073–1099 (2021).

    Article  Google Scholar 

  11. Raymond, C. et al. Increasing spatiotemporal proximity of heat and precipitation extremes in a warming world quantified by a large model ensemble. Environ. Res. Lett. 17, 035005 (2022).

    Article  Google Scholar 

  12. White, K., Habib, R. & Hardisty, D. J. How to SHIFT consumer behaviors to be more sustainable: a literature review and guiding framework. J. Mark. 83, 22–49 (2019).

    Article  Google Scholar 

  13. Hallak, R., Onur, I. & Lee, C. Consumer demand for healthy beverages in the hospitality industry: examining willingness to pay a premium, and barriers to purchase. PLoS ONE 17, e0267726 (2022).

    Article  CAS  Google Scholar 

  14. Hughner, R. S., McDonagh, P., Prothero, A., Shultz, C. J. II & Stanton, J. Who are organic food consumers? A compilation and review of why people purchase organic food. J. Consum. Behav. 6, 94–110 (2007).

    Article  Google Scholar 

  15. Rockström, J. et al. Sustainable intensification of agriculture for human prosperity and global sustainability. Ambio 46, 4–17 (2017).

    Article  Google Scholar 

  16. Vial, G. Understanding digital transformation: a review and a research agenda. J. Strat. Inf. Syst. 28, 118–144 (2019).

    Article  Google Scholar 

  17. Sharma, R., Kamble, S. S., Gunasekaran, A., Kumar, V. & Kumar, A. A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Comput. Oper. Res. 119, 104926 (2020).

    Article  Google Scholar 

  18. Yamakami, T. An experimental implementation of an edge-based AI engine with edge-cloud coordination. In 2018 18th International Symposium on Communications and Information Technologies (ISCIT) 442–446 (IEEE, 2018).

  19. McCarthy, J. Programs with common sense. In Proc. Teddington Conference on the Mechanization of Thought Processes 75–91 (Her Majesty’s Stationary Office, 1959).

  20. Samuel, A. L. Some studies in machine learning using the game of checkers. IBM J. Res. Dev. 3, 210–229 (1959).

    Article  Google Scholar 

  21. Dechter, R. Learning while searching in constraint-satisfaction-problems. In Proc. Fifth AAAI National Conference on Artificial Intelligence 178–183 (AAAI Press, 1986).

  22. Liu, X. et al. RENO: a high-efficient reconfigurable neuromorphic computing accelerator design. In Proc. 52nd ACM/EDAC/IEEE Design Automation Conference (DAC) 1–6 (2015).

  23. Moradi, S., Qiao, N., Stefanini, F. & Indiveri, G. A scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors (DYNAPs). IEEE Trans. Biomed. Circuits Syst. 12, 106–122 (2018).

    Article  Google Scholar 

  24. Akopyan, F. et al. TrueNorth: design and tool flow of a 65 mW 1 million neuron programmable neurosynaptic chip. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 34, 1537–1557 (2015).

    Article  Google Scholar 

  25. Davies, M. E. et al. Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38, 82–99 (2018).

    Article  Google Scholar 

  26. Frenkel, C., Legat, J. D. & Bol, D. MorphIC: a 65-nm 738k-synapse/mm2 quad-core binary-weight digital neuromorphic processor with stochastic spike-driven online learning. IEEE Trans. Biomed. Circuits Syst. 13, 999–1010 (2019).

    Article  Google Scholar 

  27. Gebregiorgis, A. et al. Tutorial on memristor-based computing for smart edge applications. Memories 4, 100025 (2023).

    Google Scholar 

  28. Brown, T. B. et al. Language models are few-shot learners. In Proc. 34th International Conference on Neural Information Processing Systems (eds Larochelle, H. et al.) 1877–1901 (Curran Associates, 2020).

  29. Patterson, D. et al. The carbon footprint of machine learning training will plateau, then shrink. In Computer Vol. 55, 18–28 (IEEE Computer Society, 2022).

  30. Hamdioui, S. et al. Memristor for computing: myth or reality? In Design, Automation & Test in Europe Conference & Exhibition (DATE) 722–731 (IEEE, 2017).

  31. Singh, A. et al. Low-power memristor-based computing for edge-AI applications. In Proc. IEEE International Symposium on Circuits and Systems (ISCAS) 1–5 (IEEE 2021).

  32. Singh, A. et al. CIM-based robust logic accelerator using 28 nm STT-MRAM characterization chip tape-out. In Proc. IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS) 1–5 (IEEE, 2022).

  33. Xu, Y. et al. Artificial intelligence: a powerful paradigm for scientific research. Innovation https://doi.org/10.1016/j.xinn.2021.100179 (2021).

  34. Leroux, S., Simoens, P., Lootus, M., Thakore, K. & Sharma, A. TinyMLOps: operational challenges for widespread edge AI adoption. In Proc. IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) 1003–1010 (IEEE, 2022).

  35. Kakani, V., Nguyen, V. H., Kumar, B. P., Kim, H. & Pasupuleti, V. R. A critical review on computer vision and artificial intelligence in food industry. J. Agric. Food Res. 2, 100033 (2020).

    Google Scholar 

  36. Ninomiya, S. High-throughput field crop phenotyping: current status and challenges. Breed. Sci. 72, 3–18 (2022).

    Article  Google Scholar 

  37. Cisternas, I., Velásquez, I., Caro, A. & Rodríguez, A. Systematic literature review of implementations of precision agriculture. Comput. Electron. Agric. 176, 105626 (2020).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  39. Pérez-Ruíz, M., Slaughter, D. C., Fathallah, F. A., Gliever, C. J. & Miller, B. J. Co-robotic intra-row weed control system. Biosyst. Eng. 126, 45–55 (2014).

    Article  Google Scholar 

  40. Gázquez, J. A., Castellano, N. N. & Manzano-Agugliaro, F. Intelligent low cost telecontrol system for agricultural vehicles in harmful environments. J. Clean. Prod. 113, 204–215 (2016).

    Article  Google Scholar 

  41. Akhavizadegan, F., Ansarifar, J., Wang, L., Huber, I. & Archontoulis, S. V. A time-dependent parameter estimation framework for crop modeling. Sci. Rep. 11, 11437 (2021).

    Article  CAS  Google Scholar 

  42. van Klompenburg, T., Kassahun, A. & Catal, C. Crop yield prediction using machine learning: a systematic literature review. Comput. Electron. Agric. 177, 105709 (2020).

    Article  Google Scholar 

  43. Bock, C. H., Barbedo, J. G. A., Del Ponte, E. M., Bohnenkamp, D. & Mahlein, A.-K. From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy. Phytopathol. Res. 2, 9 (2020).

    Article  Google Scholar 

  44. Dandrifosse, S., Bouvry, A., Leemans, V., Dumont, B. & Mercatoris, B. Imaging wheat canopy through stereo vision: overcoming the challenges of the laboratory to field transition for morphological features extraction. Front. Plant Sci. https://doi.org/10.3389/fpls.2020.00096 (2020).

  45. Ye, Y., Bruzzone, L., Shan, J., Bovolo, F. & Zhu, Q. Fast and robust matching for multimodal remote sensing image registration. IEEE Trans. Geosci. Remote Sens. 57, 9059–9070 (2019).

    Article  Google Scholar 

  46. Carlier, A., Dandrifosse, S., Dumont, B. & Mercatoris, B. Comparing CNNs and PLSr for estimating wheat organs biophysical variables using proximal sensing. Front. Plant Sci. https://doi.org/10.3389/fpls.2023.1204791 (2023).

  47. Raj, M. et al. A survey on the role of Internet of Things for adopting and promoting Agriculture 4.0. J. Netw. Comput. Appl. 187, 103107 (2021).

    Article  Google Scholar 

  48. Tardaguila, J., Stoll, M., Gutiérrez, S., Proffitt, T. & Diago, M. P. Smart applications and digital technologies in viticulture: a review. Smart Agric. Technol. 1, 100005 (2021).

    Article  Google Scholar 

  49. Khanna, A. & Kaur, S. Evolution of Internet of Things (IoT) and its significant impact in the field of precision agriculture. Comput. Electron. Agric. 157, 218–231 (2019).

    Article  Google Scholar 

  50. Bao, J. & Xie, Q. Artificial intelligence in animal farming: a systematic literature review. J. Clean. Prod. 331, 129956 (2022).

    Article  Google Scholar 

  51. Yépez-Ponce, D. F., Salcedo, J. V., Rosero-Montalvo, P. D. & Sanchis, J. Mobile robotics in smart farming: current trends and applications. Front. Artif. Intell. https://doi.org/10.3389/frai.2023.1213330 (2023).

  52. Duong, L. N. K. et al. A review of robotics and autonomous systems in the food industry: from the supply chains perspective. Trends Food Sci. Technol. 106, 355–364 (2020).

    Article  CAS  Google Scholar 

  53. Food robotics: global market unit volume 2020–2030. Statista https://www.statista.com/statistics/1290022/food-robotics-global-market-unit-volume/ (2022).

  54. Zhou, Z. et al. Edge intelligence: paving the last mile of artificial intelligence with edge computing. Proc. IEEE 107, 1738–1762 (2019).

    Article  Google Scholar 

  55. Lin, Y. H. et al. Performance impacts of inalog ReRAM non-ideality on neuromorphic computing. IEEE Trans. Electron Devices 66, 1289–1295 (2019).

    Article  CAS  Google Scholar 

  56. Mohanan, V., Budiarto, R. & Aldmour, I. Powering the Internet of Things with 5G Networks (IGI Global, 2017).

  57. Tzachor, A., Devare, M., King, B., Avin, S. & Ó hÉigeartaigh, S. Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities. Nat. Mach. Intell. 4, 104–109 (2022).

    Article  Google Scholar 

  58. Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).

    Article  Google Scholar 

  59. Silvestro, D., Goria, S., Sterner, T. & Antonelli, A. Improving biodiversity protection through artificial intelligence. Nat. Sustain. 5, 415–424 (2022).

    Article  Google Scholar 

  60. Deichmann, U., Goyal, A. & Mishra, D. Will digital technologies transform agriculture in developing countries? Agr. Econ. 47, 21–33 (2016).

    Article  Google Scholar 

  61. Blount-Dorn, K., Detroit Food Policy Council and Lindsey Scalera, Ecology Center Modeling an Equitable Michigan Food System (Michigan State Univ., 2018); https://www.canr.msu.edu/news/modeling-an-equitable-michigan-food-system

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Acknowledgements

A.-K.M. was partially funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—EXC 2070–390732324. S.H. acknowledges support from the EU Horizon Europe research and innovation programme (grant agreement no. 101070374). C.H.B was supported by the USDA-ARS National Programs through CRIS project 6042-21220-014-000D.

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Author notes

  1. Louis Kouadio

    Present address: Africa Rice Center (AfricaRice), Bouake, Côte d’Ivoire

Authors and Affiliations

  1. SPHERES Research Unit, Department of Environmental Sciences and Management, University of Liège, Arlon, Belgium

    Moussa El Jarroudi

  2. Centre for Applied Climate Sciences, Institute for Life and the Environment, University of Southern Queensland, Toowoomba, Queensland, Australia

    Louis Kouadio

  3. Office of the Rector, Maison du Savoir, Université du Luxembourg, Esch-sur-Alzette, Grand Duchy of Luxembourg

    Philippe Delfosse

  4. ARS-US Horticultural Research Laboratory, USDA, Ft. Pierce, FL, USA

    Clive H. Bock

  5. Institute of Sugar Beet Research, Göttingen, Germany

    Anne-Katrin Mahlein

  6. SPHERES Research Unit, Department of Geography, University of Liège, Liège, Belgium

    Xavier Fettweis

  7. Gembloux Agro-Bio Tech, Biosystems Dynamics and Exchanges, TERRA Teaching and Research Centre, University of Liège, Gembloux, Belgium

    Benoit Mercatoris

  8. Lycée Technique Agricole de Gilsdorf, Gilsdorf, Grand Duchy of Luxembourg

    Frank Adams

  9. North Oldmoss Croft, Fyvie, Turriff, UK

    Jillian M. Lenné

  10. Department of Quantum and Computer Engineering, Delft University of Technology, Delft, Netherlands

    Said Hamdioui

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Contributions

M.E.J., L.K., P.D. and S.H. contributed to the conceptualization and led the writing and revisions. C.H.B., A.-K.M., X.F., B.M., F.A. and J.M.L. contributed to the writing and revisions. All authors have read and agreed to the published version of the paper.

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Correspondence to Moussa El Jarroudi, Xavier Fettweis or Said Hamdioui.

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Nature Sustainability thanks Xu Chen, Yu Jiang and Shangpeng Sun for their contribution to the peer review of this work.

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El Jarroudi, M., Kouadio, L., Delfosse, P. et al. Leveraging edge artificial intelligence for sustainable agriculture. Nat Sustain 7, 846–854 (2024). https://doi.org/10.1038/s41893-024-01352-4

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