Abstract
The burgeoning global population constricted arable land availability, exacerbated farming input expenditures, and a dwindling labor workforce underscore the imperative for pioneering advancements within the realm of agriculture and cultivation. AgriTech represents the synergistic integration of cutting-edge technologies and human-computer interaction (HCI) into traditional agricultural methodologies, poised to usher in a transformative era in farming practices. It heralds a promising frontier for the implementation of intelligent farming techniques. Within this scholarly exposition, we delve into the profound challenges that beset the domain of intelligent agriculture and advanced agricultural technologies. We proffer an in-depth exploration of historical developments, leveraging innovative applications of artificial intelligence (AI), automation, robotics, and the Internet of Things (IoT) to propel the paradigm of intelligent farming forward. Furthermore, we elucidate the impediments intrinsic to these technologies and proffer potential remedies encapsulated within the purview of AgriTech. This research not only serves as a comprehensive elucidation of the multifaceted intricacies within the domain of AgriTech but also serves as a launching pad, fostering fertile grounds for the cultivation of future AgriTech innovations.
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References
Sharma, V., Tripathi, A.K., Mittal, H.: Technological revolutions in smart farming: current trends, challenges & future directions. Comput. Electron. Agric. 201, 107217 (2022)
Bai, Y., Zhang, B., Xu, N., Zhou, J., Shi, J., Diao, Z.: Vision-based navigation and guidance for agricultural autonomous vehicles and robots: a review. Comput. Electron. Agric. 205, 107584 (2023)
Sinha, B.B., Dhanalakshmi, R.: Recent advancements and challenges of Internet of Things in smart agriculture: a survey. Future Gener. Comput. Syst. 126, 169–184 (2022)
Depta, L.: Global food waste and its environmental impact. Reset (2018). https://en.reset.org/global-food-waste-and-its-environmental-impact-09122018/. Accessed 03 Sept 2023
Bradford, K.J., et al.: The dry chain: Reducing postharvest losses and improving food safety in humid climates. Trends Food Sci. Technol. 71, 84–93 (2018)
Gunasekera, D., Parsons, H., Smith, M.: Post-harvest loss reduction in Asia-Pacific developing economies. J. Agribus. Dev. Emerg. Econ. 7(3), 303–317 (2017)
Goyal, A., Lock, E., Moorthy, D., Perera, R.: Saving Southeast Asia’s crops: Four key steps toward food security. McKinsey & Company Inc. (2023). https://www.mckinsey.com/industries/agriculture/our-insights/saving-southeast-asias-crops-four-key-steps-toward-food-security. Accessed 03 Sept 2023
Bland, R., Ganesan, V., Hong, E., Kalanik, J.: Trends driving automation on the farm. McKinsey & Company Inc. (2023). https://www.mckinsey.com/industries/agriculture/our-insights/trends-driving-automation-on-the-farm. Accessed 04 Sept 2023
Ferreira, N., Fiocco, D., Ganesan, V., de la Serrana Lozano, M.G., Mokodsi, A.L., Gryschek, O.: Global Farmer Insights. McKinsey & Company Inc. (2022). https://globalfarmerinsights2022.mckinsey.com/#autores. Accessed 04 Sept 2023
Frost, C., Jayaram, J., Pai, G.: What climate-smart agriculture means for smallholder farmers. McKinsey & Company Inc. (2023). https://www.mckinsey.com/industries/agriculture/our-insights/what-climate-smart-agriculture-means-for-smallholder-farmers. Accessed 04 Sept 2023
Goedde, L., Katz, J., Menard, A., Revellat, J.: Agriculture’s connected future: how technology can yield new growth. McKinsey & Company Inc. (2020). https://www.mckinsey.com/industries/agriculture/our-insights/agricultures-connected-future-how-technology-can-yield-new-growth. Accessed 04 Sept 2023
Ciarfuglia, T.A., Marian Motoi, I., Saraceni, L., Nardi, D.: Pseudo-label generation for agricultural robotics applications. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1685–1693. IEEE, New Orleans (2022)
Ciarfuglia, T.A., Motoi, I.M., Saraceni, L., Fawakherji, M., Sanfeliu, A., Nardi, D.: Weakly and semi-supervised detection, segmentation and tracking of table grapes with limited and noisy data. Comput. Electron. Agric. 205, 107624 (2023)
Jackulin, C., Murugavalli, S.: A comprehensive review on detection of plant disease using machine learning and deep learning approaches. Meas.: Sens. 24, 100441 (2022)
Metre, V.A.: Research review on plant leaf disease detection utilizing swarm intelligence. Turk. J. Comput. Math. Educ. (TURCOMAT) 12(10), 177–185 (2021)
Jerhamre, E., Carlberg, C.J.C., van Zoest, V.: Exploring the susceptibility of smart farming: identified opportunities and challenges. Smart Agric. Technol. 2, 100026 (2022)
Meier, J., Mauser, W., Hank, T., Bach, H.: Assessments on the impact of high-resolution-sensor pixel sizes for common agricultural policy and smart farming services in European regions. Comput. Electron. Agric. 169, 105205 (2020)
Kamienski, C., et al.: Smart water management platform: IoT-based precision irrigation for agriculture. Sensors 19(2), 276 (2019)
Kleinschmidt, J.H., Kamienski, C., Prati, R.C., Kolehmainen, K., Aguzzi, C.: End-to-end security in the IoT computing continuum: perspectives in the SWAMP project. In: 9th Latin-American Symposium on Dependable Computing (LADC), pp. 1–2 IEEE (2019)
Rosero-Montalvo, P.D., Gordillo-Gordillo, C.A., Hernandez, W.: Smart farming robot for detecting environmental conditions in a greenhouse. IEEE Access 11, 57843–57853 (2023)
Boukens, M., Boukabou, A., Chadli, M.: A real time self-tuning motion controller for mobile robot systems. IEEE/CAA J. Autom. Sinica 6(1), 84–96 (2019)
Jiang, J., Moallem, M.: Development of an intelligent LED lighting control testbed for IoT-based smart greenhouses. In: IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, pp. 5226–5231. IEEE, Singapore (2020)
Durmus, H., and Günes, E.O.: Integration of the mobile robot and Internet of Things to collect data from the agricultural fields. In: 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), pp. 1–5. IEEE, Istanbul (2019)
Acknowledgments
This work was partially supported by the Brain Pool Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2019H1D3A1A01071115), and partially supported by Korea Evaluation Institute of Industrial Technology (KEIT) grant funded by the Korea government (MOTIE). (No. 1415181754, 3D semantic camera module development capable of material and property recognition).
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Mishra, A., Kim, S. (2024). A Comprehensive Survey on AgriTech to Pioneer the HCI-Based Future of Farming. In: Choi, B.J., Singh, D., Tiwary, U.S., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2023. Lecture Notes in Computer Science, vol 14531. Springer, Cham. https://doi.org/10.1007/978-3-031-53827-8_28
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