IoT-Enabled Autonomous Agriculture Monitoring Rover for Smart Farming: A Comprehensive Literature Review

Abstract

The rapid development of the Internet of Things (IoT), autonomous robotics, artificial intelligence (AI), embedded systems, wireless communication, and precision agriculture has created new opportunities for improving agricultural productivity and resource utilization. Conventional agricultural monitoring relies extensively on manual field inspection, which is time-consuming, labor-intensive, and often unable to provide continuous information about crop and soil conditions. Autonomous agricultural rovers provide an alternative approach by enabling mobile sensing, real-time environmental monitoring, crop observation, navigation, and precision agricultural operations. This paper presents a comprehensive literature review of IoT-enabled autonomous agricultural rovers for smart farming, with particular emphasis on recent developments reported in IEEE literature. Existing research is analyzed according to rover architecture, sensing technologies, navigation techniques, communication technologies, IoT platforms, agricultural applications, AI integration, and autonomy levels. Recent studies have demonstrated the feasibility of ESP32-based IoT rovers, ROS-based agricultural monitoring robots, autonomous navigation systems, precision irrigation platforms, soil-monitoring robots, and AI-enabled agricultural rovers. However, several limitations remain, including restricted autonomous navigation in unstructured environments, limited multi-parameter sensing, communication reliability in rural areas, high computational requirements for AI, energy limitations, and insufficient integration of monitoring with autonomous decision-making. Based on the reviewed literature, a research gap is identified for a low-cost, modular, IoT-enabled autonomous agricultural monitoring rover capable of simultaneously performing soil and environmental sensing, crop monitoring, obstacle avoidance, real-time wireless communication, location-aware operation, and intelligent decision support.

Introduction

1. The IoT-Enabled Autonomous Agriculture Monitoring Rover is a smart agricultural system designed to improve crop monitoring and farming efficiency through automation, Internet of Things (IoT) technology, robotics, and artificial intelligence (AI). Traditional farming relies heavily on manual observation and stationary sensors, which can be time-consuming and may fail to identify changes in soil conditions, environmental parameters, and crop health at an early stage.

   The main objective of the proposed system is to develop an autonomous mobile rover capable of collecting agricultural data from multiple locations, monitoring crop and soil conditions, detecting obstacles, and transmitting real-time information to farmers for informed decision-making.

   The literature review examines recent IEEE research on autonomous agricultural rovers, IoT-enabled precision farming, ROS-based monitoring robots, precision watering, and AI-enabled crop monitoring. Existing studies demonstrate progress in autonomous navigation, environmental sensing, wireless connectivity, and agricultural automation. However, many systems focus on specific tasks, such as sowing, irrigation, or environmental monitoring, rather than integrating comprehensive monitoring and intelligent decision-making.

   The proposed rover consists of soil and environmental sensors, a crop-imaging unit, an ESP32 or STM32 controller, motor drivers, geared motors, GPS-based navigation, obstacle-detection sensors, wireless communication, and an IoT dashboard. Sensors collect information such as soil moisture, temperature, humidity, pH, light intensity, and crop condition. The controller processes the collected data and transmits it through communication technologies such as Wi-Fi, LoRa, or 4G to a cloud platform or mobile application.

   Artificial intelligence can further enhance the system by analyzing crop images to identify plant diseases, weeds, nutrient deficiencies, and growth stages. Autonomous navigation technologies, including GPS, RTK-GPS, ultrasonic sensors, LiDAR, and computer vision, can help the rover move through agricultural fields while avoiding obstacles and following planned paths.

   The research identifies several challenges in existing systems, including limited integration of monitoring functions, inadequate mobile sensing, restricted intelligent decision-making, navigation difficulties, unreliable rural connectivity, energy consumption, and high implementation costs. The proposed framework addresses these challenges through a modular, low-cost architecture that combines mobile sensing, autonomous movement, real-time IoT monitoring, and intelligent agricultural analysis.

   Future improvements may include advanced crop-disease detection, precision irrigation, automated path planning, long-range communication, energy-efficient operation, and field testing under different agricultural conditions.

   In conclusion, the IoT-enabled autonomous agriculture monitoring rover offers a promising approach to precision farming by combining robotics, sensors, wireless communication, and AI. It can help farmers monitor field conditions more efficiently, identify potential crop problems earlier, and make data-driven decisions. With further development in navigation, intelligent analysis, and cost optimization, the system could become a practical tool for sustainable and efficient agriculture.

    

Conclusion

This literature review investigated recent developments in IoT-enabled autonomous agricultural rovers for smart farming, with emphasis on recent IEEE research. The reviewed studies demonstrate substantial progress in autonomous navigation, IoT connectivity, environmental sensing, precision irrigation, agricultural operations, soil monitoring and AI-enabled crop monitoring. Recent work demonstrates the feasibility of affordable autonomous agricultural platforms, while other studies demonstrate ROS-based navigation, environmental monitoring, precision watering and AI-enabled low-cost robotics. Nevertheless, significant opportunities remain in integrating mobile sensing, autonomous navigation, IoT communication, AI-based crop analysis, spatial soil monitoring and intelligent decision-making within a single low-cost platform. An IoT-enabled autonomous agriculture monitoring rover is therefore a promising research direction for small- and medium-scale farms. Such a system can reduce dependence on manual monitoring, improve resource utilization, provide continuous agricultural intelligence and contribute to sustainable precision agriculture.

References

[1] A. K. Sanal, A. P. R., A. Chandran, J. Anoj, and A. Francis, “IoT-Enabled Autonomous Rover for Small-Scale Precision Farming,” in Proc. 2025 Int. Conf. Power, Instrumentation, Control and Computing (PICC), 2025, doi: 10.1109/PICC67314.2025.11291327. [2] F. Xu, X. Yin, L. Wang, and Y. Shen, “Agricultural Monitoring Robot Based on Robot Operating System,” in Proc. 2025 IEEE 3rd Int. Conf. Electrical, Automation and Computer Engineering (ICEACE), 2025, pp. 1770–1775, doi: 10.1109/ICEACE67491.2025.11439057. [3] S. Saravanan et al., “Design of an Intelligent Autonomous and Manually Operated Agricultural Rover with Automated Navigation, Precision Watering, and Real-Time Monitoring,” in Proc. 2025 IEEE Silchar Subsection Conf. (SILCON), 2025, doi: 10.1109/SILCON67893.2025.11327010. [4] A. Landge, P. Patil, S. Jadhav, S. Jadhav, and D. A. Jakkan, “AGROBOT: An IoT-Enabled Autonomous Agricultural Robot for Precision Farming,” in Proc. 2025 IEEE Pune Section Int. Conf. (PuneCon), 2025, doi: 10.1109/PuneCon67554.2025.11378815. [5] C. D. Andrei and S. Dorin, “Low-Cost Agricultural Robotics: An AI-Enabled Autonomous Rover,” in Proc. 2025 IEEE Int. Symp. Design and Technology of Electronics Packages (SIITME), 2025, pp. 269–272, doi: 10.1109/SIITME67657.2025.11293697. [6] A. M. Ruzic et al., “Autonomous Driving Strategy for a Specialized Four-Wheel Differential-Drive Agricultural Rover,” AgriEngineering, 2024. [7] S. Martelli, V. Martini, F. Mocera, and A. Soma, “Co-Simulation Model of an Autonomous Driving Rover for Agricultural Applications,” Robotics, vol. 14, no. 9, 2025. [8] F. Micheletti, G. Agati, S. Cremasco, F. Visentin, and R. Muradore, “In-field autonomous UV-B irradiation and phenolic compounds optical detection of lettuce crops by an agricultural rover prototype,” in Proc. 2025 IEEE Int. Workshop Metrology for Agriculture and Forestry (MetroAgriFor), 2025, pp. 378–382, doi: 10.1109/MetroAgriFor66923.2025.11512400. [9] J. L. Cleetus et al., “Enhancing the Farmland Rover for Muddy Terrain with IoT,” 2025. [10] L. Joseph, S. Joseph, S. H. Abraham, and A. George, “An Autonomous Agriculture Robot Using IoT,” Int. J. Computer Sciences and Engineering, vol. 8, no. 7. [11] A. Bechar and C. Vigneault, “Agricultural robots for field operations: Concepts and components,” Biosyst. Eng., vol. 149, pp. 94–111, 2016. [12] A. Bechar and C. Vigneault, “Agricultural robots for field operations. Part 2: Operations and systems,” Biosyst. Eng., vol. 153, pp. 110–128, 2017. [13] M. Ayaz, M. Ammad-Uddin, Z. Sharif, A. Mansour, and E. M. Aggoune, “Internet-of-Things (IoT)-based smart agriculture: Toward making the fields talk,” IEEE Access, vol. 7, pp. 129551–129583, 2019. [14] O. Elijah, T. A. Rahman, I. Orikumhi, C. Y. Leow, and M. N. Hindia, “An overview of Internet of Things (IoT) and data analytics in agriculture: Benefits and challenges,” IEEE Internet Things J., vol. 5, no. 5, pp. 3758–3773, Oct. 2018. [15] S. Wolfert, L. Ge, C. Verdouw, and M.-J. Bogaardt, “Big data in smart farming – A review,” Agric. Syst., vol. 153, pp. 69–80, 2017. [16] A. Kamilaris and F. X. Prenafeta-Boldu, “Deep learning in agriculture: A survey,” Comput. Electron. Agric., vol. 147, pp. 70–90, 2018. [17] S. P. Mohanty, D. P. Hughes, and M. Salathe, “Using deep learning for image-based plant disease detection,” Front. Plant Sci., vol. 7, Art. no. 1419, 2016. [18] L. F. P. Oliveira, A. P. Moreira, and M. F. Silva, “Advances in agriculture robotics: A state-of-the-art review and challenges ahead,” Robotics, vol. 10, no. 2, Art. no. 52, 2021. [19] B. S. Sinha and R. Dhanalakshmi, “Recent advancements and challenges of Internet of Things in smart agriculture: A survey,” Future Gener. Comput. Syst., vol. 126, pp. 169–184, 2022. [20] M. Quigley et al., “ROS: An open-source Robot Operating System,” in Proc. ICRA Workshop Open Source Softw., Kobe, Japan

Copyright

Copyright © 2026 Chaitra A S, Venugopal Gowda V, Vinay H, Jeevan T, Puneeth M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.