journal article Open Access Apr 09, 2026

Agentic AI-Based IoT Precision Agriculture Framework—Our Vision and Challenges

AgriEngineering Vol. 8 No. 4 pp. 147 · MDPI AG
View at Publisher Save 10.3390/agriengineering8040147
Abstract
Accurate, timely, and resource-efficient decision-making is critical for sustainable precision agriculture. This paper proposes an agentic AI-based Internet of Things (IoT) framework that enables coordinated, closed-loop perception–decision–action processes across heterogeneous sensing and actuation components. The framework models agricultural systems as distributed collections of goal-driven agents responsible for multimodal sensing, uncertainty-aware reasoning, and adaptive decision-making. To provide a structured foundation, the proposed architecture is formalized within a Multi-Agent Partially Observable Markov Decision Process (MPOMDP) perspective, enabling systematic treatment of coordination, uncertainty, and decision policies. The framework integrates multimodal information sources, including vision-based perception and environmental sensing, and defines mechanisms for their fusion and use in system-level decision-making. A proof-of-concept instantiation is presented using publicly available datasets, combining visual perception models and tabular reasoning models within the proposed agentic workflow. The experiments are designed to demonstrate the feasibility, modularity, and coordination capabilities of the framework, rather than to benchmark predictive performance or provide field-validated evaluation. The results illustrate how multimodal information can be integrated to support adaptive and resource-aware decision processes. Finally, the paper discusses key challenges and outlines directions for future work, including real-world deployment, integration with physical actuation systems, and validation under operational conditions.
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References
64
[1]
Adve "Advancing AI in Agriculture through Large-Scale Collaborative Research" Commun. ACM (2025) 10.1145/3760437
[2]
Big Data in Smart Farming – A review

Sjaak Wolfert, Lan Ge, Cor Verdouw et al.

Agricultural Systems 2017 10.1016/j.agsy.2017.01.023
[3]
Liakos, K.G., Busato, P., Moshou, D., Pearson, S., and Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18. 10.3390/s18082674
[4]
Rose, D.C., and Chilvers, J. (2018). Agriculture 4.0: Broadening Responsible Innovation in an Era of Smart Farming. Front. Sustain. Food Syst., 2. 10.3389/fsufs.2018.00087
[5]
Lermen "An overview of agriculture 4.0 development: Systematic review of descriptions, technologies, barriers, advantages, and disadvantages" Comput. Electron. Agric. (2021) 10.1016/j.compag.2021.106405
[6]
The digitization of agricultural industry – a systematic literature review on agriculture 4.0

Rabiya Abbasi, Pablo Martinez, Rafiq Ahmad

Smart Agricultural Technology 2022 10.1016/j.atech.2022.100042
[7]
Choudhary "An overview of smart agriculture using internet of things (IoT) and web services" Environ. Sustain. Indic. (2025)
[8]
Miller, T., Mikiciuk, G., Durlik, I., Mikiciuk, M., Łobodzińska, A., and Śnieg, M. (2025). The IoT and AI in agriculture: The time is now—A systematic review of smart sensing technologies. Sensors, 25. 10.3390/s25123583
[9]
Deep learning in agriculture: A survey

Andreas Kamilaris, Francesc X. Prenafeta-Boldú

Computers and Electronics in Agriculture 2018 10.1016/j.compag.2018.02.016
[10]
Zhu, H., Qin, S., Su, M., Lin, C., Li, A., and Gao, J. (2025). Harnessing large vision and language models in agriculture: A review. Front. Plant Sci., 16. 10.3389/fpls.2025.1579355
[11]
Lytridis, C., Kaburlasos, V.G., Pachidis, T., Manios, M., Vrochidou, E., Kalampokas, T., and Chatzistamatis, S. (2021). An overview of cooperative robotics in agriculture. Agronomy, 11. 10.3390/agronomy11091818
[12]
An, W., Wu, D., Ci, S., Luo, H., Adamchuk, V., and Xu, Z. (2017). Agriculture cyber-physical systems. Cyber-Physical Systems, Elsevier. 10.1016/b978-0-12-803801-7.00025-0
[13]
Weraikat, D., Šorič, K., Žagar, M., and Sokač, M. (2024). Data analytics in agriculture: Enhancing decision-making for crop yield optimization and sustainable practices. Sustainability, 16. 10.20944/preprints202406.1042.v1
[14]
Kassam "Global achievements in soil and water conservation: The case of Conservation Agriculture" Int. Soil Water Conserv. Res. (2014) 10.1016/s2095-6339(15)30009-5
[15]
Clark "Comparative analysis of environmental impacts of agricultural production systems, agricultural input efficiency, and food choice" Environ. Res. Lett. (2017) 10.1088/1748-9326/aa6cd5
[16]
An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges

Olakunle Elijah, Tharek Abdul Rahman, Igbafe Orikumhi et al.

IEEE Internet of Things Journal 2018 10.1109/jiot.2018.2844296
[17]
Patil "IoT based smart farming system" Int. J. Adv. Res. Ideas Innov. Technol. (2021)
[18]
Spasev, V., Dimitrovski, I., Kitanovski, I., and Chorbev, I. (2023). Semantic segmentation of remote sensing images: Definition, methods, datasets and applications. International Conference on ICT Innovations, Springer. 10.1007/978-3-031-54321-0_9
[19]
Gatkal "Review of UAVs for efficient agrochemical spray application" Int. J. Agric. Biol. Eng. (2025)
[20]
Jagadeeswari, M., Manikandababu, C.S., S, K., R, P., and S, M. (2022, January 21–23). Artificial Intelligence based Crop Recommendation System. Proceedings of the 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India. 10.1109/icirca54612.2022.9985645
[21]
Jakkani "Artificial Intelligence and its Applications in the Field of Internet of Things (Iot)" Int. J. Res. Sci. Eng. (2024) 10.55529/ijrise.45.49.61
[22]
Lan, J., and Ban, Q. (2025). The Farm-Level Economic and Environmental Benefits of Precision Agriculture Technology Adoption: A Meta-Analysis of Global Evidence. Sustainability, 17. 10.3390/su172411223
[23]
Atapattu, A.J., Perera, L.K., Nuwarapaksha, T.D., Udumann, S.S., and Dissanayaka, N.S. (2024). Challenges in Achieving Artificial Intelligence in Agriculture. Artificial Intelligence Techniques in Smart Agriculture, Springer Nature. 10.1007/978-981-97-5878-4_2
[24]
Yu, P., Teng, F., Zhu, W., Shen, C., Chen, Z., and Song, J. (2025). Cloud–edge–device collaborative computing in smart agriculture: Architectures, applications, and future perspectives. Front. Plant Sci., 16. 10.3389/fpls.2025.1668545
[25]
Agyeman "A semi-centralized multi-agent RL framework for efficient irrigation scheduling" Control Eng. Pract. (2025) 10.1016/j.conengprac.2024.106183
[26]
Srinivasu, P.N., Pavate, A., JayaLakshmi, G., Shafi, J., Choi, J., and Ijaz, M.F. (2026). Agentic AI for smart and sustainable precision agriculture. Front. Plant Sci., 16. 10.3389/fpls.2025.1706428
[27]
Dornaika "Agentic AI: A Comprehensive Survey of Architectures, Applications, and Future Directions" Artif. Intell. Rev. (2025) 10.1007/s10462-025-11422-4
[28]
Unmanned Aerial Vehicle for Precision Agriculture: A Review

Francesco Toscano, Costanza Fiorentino, Nicola Capece et al.

IEEE Access 2024 10.1109/access.2024.3401018
[29]
"Recent Advancements in Drone-Based Remote Sensing for Precision Agriculture: A Mini-Review of Applications, Challenges, and Opportunities" Asian J. Res. Agric. For. (2025) 10.9734/ajraf/2025/v11i4461
[30]
Sreenatha "Applications of Unmanned Aerial Vehicles (UAVs) in Agriculture: A Review" Int. J. Res. Agron. (2025) 10.33545/2618060x.2025.v8.i9sd.3843
[31]
Anam "A systematic review of UAV and AI integration for targeted disease detection, weed management, and pest control in precision agriculture" Smart Agric. Technol. (2024) 10.1016/j.atech.2024.100647
[32]
Safaeinejad, M., Ghasemi-Nejad-Raeini, M., and Taki, M. (2025). Reducing energy and environmental footprint in agriculture: A study on drone spraying vs. conventional methods. PLoS ONE, 20. 10.1371/journal.pone.0323779
[33]
Integration of smart sensors and IOT in precision agriculture: trends, challenges and future prospectives

Sheikh Mansoor, Shahzad Iqbal, Simona M. Popescu et al.

Frontiers in Plant Science 10.3389/fpls.2025.1587869
[34]
Agrawal, J., and Arafat, M.Y. (2024). Transforming Farming: A Review of AI-Powered UAV Technologies in Precision Agriculture. Drones, 8. 10.3390/drones8110664
[35]
Xing, Y., Liu, X., and Wang, X. (2026). Integrating UAVs, satellite remote sensing, and machine learning in precision agriculture: Pathways to sustainable food production, resource efficiency, and scalable innovation. Front. Agron., 7. 10.3389/fagro.2025.1670380
[36]
Haghighat "Multimodal language models in agriculture: A tutorial and survey" Inf. Fusion (2025) 10.1016/j.inffus.2025.104042
[37]
Sapkota "Multi-modal LLMs in agriculture: A comprehensive review" IEEE Trans. Autom. Sci. Eng. (2025) 10.1109/tase.2025.3612154
[38]
Kuska "AI for crop production—Where can large language models (LLMs) provide substantial value?" Comput. Electron. Agric. (2024) 10.1016/j.compag.2024.108924
[39]
Yu, P., and Lin, B. (2024). A Framework for Agricultural Intelligent Analysis Based on a Visual Language Large Model. Appl. Sci., 14. 10.3390/app14188350
[40]
Cantonjos "AgroAskAI: A Multi-Agentic AI Framework for Supporting Smallholder Farmers’ Enquiries Globally" Proceedings of the AAAI Conference on Artificial Intelligence (2026) 10.1609/aaai.v40i47.41458
[41]
Amjad "Agentic AI for Autonomous Soil and Fertilization Management for Agriculture Sustainability" Int. J. Innov. Sci. Technol. (2025)
[42]
Swati "Agentic AI-driven autonomous decision support system for smart agriculture" Sci. Rep. (2026) 10.1038/s41598-026-39472-w
[43]
Murad, M., Ahmed, M., din, N.u., Shahid, M.F., Siddiqui, S., Byers, D., Tanveer, M.H., and Voicu, R.C. (2026). Agentic AI Framework to Automate Traditional Farming for Smart Agriculture. AgriEngineering, 8. 10.3390/agriengineering8010008
[44]
Toskov, B., and Toskova, A. (2026). AgroNova: An Autonomous IoT Platform for Greenhouse Climate Control. Sensors, 26. 10.20944/preprints202602.1079.v1
[45]
Germer "An Agent-Based Service Architecture for Smart Greenhouses: Telemetry Analytics and Decision Support with RAG-grounded LLM Agents" Smart Agric. Technol. (2026) 10.1016/j.atech.2026.101872
[46]
Edge-enabled smart agriculture framework: Integrating IoT, lightweight deep learning, and agentic AI for context-aware farming

Muhammad Usman Tariq, Sheikh Muhammad Saqib, Tehseen Mazhar et al.

Results in Engineering 2025 10.1016/j.rineng.2025.107342
[47]
Tang "Enhanced multi agent coordination algorithm for drone swarm patrolling in durian orchards" Sci. Rep. (2025) 10.1038/s41598-025-88145-7
[48]
Ahmadi, M., Singletary, A., Burdick, J.W., and Ames, A.D. (2019, January 11–13). Safe policy synthesis in multi-agent POMDPs via discrete-time barrier functions. Proceedings of the 2019 IEEE 58th Conference on Decision and Control (CDC), Nice, France. 10.1109/cdc40024.2019.9030241
[49]
Oliehoek, F.A., and Amato, C. (2016). A Concise Introduction to Decentralized POMDPs, Springer. 10.1007/978-3-319-28929-8
[50]
Curasma "Agents for automatic control of sensors using Multi-Agent Systems and Ontologies: A scalable IoT architecture" Procedia Comput. Sci. (2024) 10.1016/j.procs.2024.06.041

Showing 50 of 64 references