Low-Cost Car-Like Autonomous Mobile Robot Under-Canopy Row Following, and Detection of Patterns Associated with Diseases in Plantain Crops
DOI:
https://doi.org/10.3991/ijoe.v22i10.61223Keywords:
Autonomous mobile robot, fuzzy control, leaves images, offline analysis, patterns recognition, plantain crops.Abstract
Labor constraints and the need for frequent visual inspection of lower plantain (Musa paradisiaca) leaves in Colombia’s Orinoquia region motivate the development of low-cost robotic tools capable of autonomous under-canopy navigation and systematic image capture. This study reports the current stage of an ongoing research effort on a low-cost, small-scale, car-like agricultural robot for autonomous under-canopy row-following and systematic image capture in plantain fields. The platform uses a modular ROS 2 workflow, a low-cost 2D LiDAR, an inertial measurement unit, and an ESP32-based actuation layer. LiDAR data were processed through an adaptive measurement cone guided by the vehicle heading error to estimate the lateral distance to the crop row during forward row-following motion, and a fuzzy controller generated steering references. Initial field tests across five trials provided preliminary evidence of consistent under-canopy navigation under the tested conditions. In the first rowfollowing segment, the median MAE and RMSE of the lateral error were 0.0258 m and 0.0342 m, respectively; after the row-to-row maneuver, they were 0.0356 m and 0.0602 m, respectively. Additionally, an upward-facing RGB camera captured under-canopy images that were processed offline to create probability-based visual summaries of leaf-condition patterns. The results show that low-cost robotic navigation can help structure under-canopy data collection for scalable crop health monitoring in plantain plantations.
References
[1] Development Co-operation Report 2023. Organisation for Economic Cooperation Development, 2023. doi: 10.1787/f6edc3c2-en.
[2] S. Das, “Transforming Agriculture: Harnessing Robotics and Drones for Sustainable Farming Solution,” J. Exp. Agric. Int., vol. 46, no. 7, pp. 219–231, June 2024, doi: 10.9734/jeai/2024/v46i72577.
[3] The State of Food and Agriculture 2022. Food Agriculture Organization, 2022. doi: 10.4060/cb9479en.
[4] T. Yat and M. Crops, Agricultural digitalization and automation in low- and middle-income countries: Evidence from ten case studies. Food Agriculture Organization, 2022. doi: 10.4060/cc2914en.
[5] A. Corallo, M. De Giovanni, M. E. Latino, and M. Menegoli, “Leveraging on technology and sustainability to innovate the supply chain: a proposal of agri-food value chain model,” SCM, vol. 29, no. 3, pp. 661–683, June 2023, doi: 10.1108/scm-12-2022-0484.
[6] S. C. Teixeira, N. O. Gomes, T. V. De Oliveira, N. F. F. Soares, and P. A. Raymundo-Pereira, “Sustainable Wearable Sensors for Plant Monitoring and Precision Agriculture.,” Anal. Chem., vol. 97, no. 28, pp. 14875–14884, June 2025, doi: 10.1021/acs.analchem.5c01565.
[7] S. Kayastha, A. Behera, J. P. Sahoo, and M. Mahapatra, “Growing Green: Sustainable Agriculture Meets Precision Farming: A Review,” BKAP, no. Of, Jan. 2024, doi: 10.18805/bkap697.
[8] C. E. Ospina-Parra, J. A. Valencia-Montoya, J. E. Cardona-Cardona, M. Duque-Rios, F. D. J. Usuga, and C. A. Alvarez Delgado, “Plantain socio-productive system (Musa AAB): An initiative to revalue agriculture in western Antioquia,” rcia, vol. 42, no. 1, p. e1256, Apr. 2025, doi: 10.22267/rcia.20254201.256.
[9] E. Raji, T. Ijomah, and O. Eyieyien, “Improving agricultural practices and productivity through extension services and innovative training programs,” Int. j. appl. res. soc. sci., vol. 6, no. 7, pp. 1297–1309, July 2024, doi: 10.51594/ijarss.v6i7.1267.
[10] J. G. Esguera, M. A. Balendres, and D. P. Paguntalan, “Overview of the <i>Sigatoka</i> leaf spot complex in banana and its current management,” T, vol. 3, no. 1, p. 0, Jan. 2024, doi: 10.48130/tp-0024-0001.
[11] N. Jiménez, S. Orellana, B. Mazon-Olivo, W. Rivas-Asanza, and I. Ramírez-Morales, “Detection of Leaf Diseases in Banana Crops Using Deep Learning Techniques,” AI, vol. 6, no. 3, p. 61, Mar. 2025, doi: 10.3390/ai6030061.
[12] D. Luna-Moreno et al., “Early Detection of the Fungal Banana Black Sigatoka Pathogen Pseudocercospora fijiensis by an SPR Immunosensor Method.,” Sensors, vol. 19, no. 3, p. 465, Jan. 2019, doi: 10.3390/s19030465.
[13] J. Ugarte Fajardo et al., “Early detection of black Sigatoka in banana leaves using hyperspectral images.,” Appl Plant Sci, vol. 8, no. 8, Aug. 2020, doi: 10.1002/aps3.11383.
[14] J. Henderson et al., “Black Sigatoka disease: new technologies to strengthen eradication strategies in Australia,” Austral. Plant Pathol., vol. 35, no. 2, p. 181, Jan. 2006, doi: 10.1071/ap06017.
[15] L. H. Jacome, “Effects of Leaf Wetness Duration and Temperature on Development of Black Sigatoka Disease on Banana Infected byMycosphaerella fijiensisvar.difformis,” Phytopathology, vol. 82, no. 5, p. 515, Jan. 1992, doi: 10.1094/phyto-82-515.
[16] D. F. Yépez-Ponce, J. V. Salcedo, P. D. Rosero-Montalvo, and J. Sanchis, “Mobile robotics in smart farming: current trends and applications.,” Front. Artif. Intell., vol. 6, Aug. 2023, doi: 10.3389/frai.2023.1213330.
[17] A. Ahmadi, L. Nardi, N. Chebrolu, and C. Stachniss, “Visual Servoing-based Navigation for Monitoring Row-Crop Fields,” Institute Of Electrical Electronics Engineers, May 2020. doi: 10.1109/icra40945.2020.9197114.
[18] M. Łągiewska and E. Panek-Chwastyk, “Integrating Remote Sensing and Autonomous Robotics in Precision Agriculture: Current Applications and Workflow Challenges,” Agronomy, vol. 15, no. 10, p. 2314, Sept. 2025, doi: 10.3390/agronomy15102314.
[19] A. Abanay, L. Masmoudi, M. El Ansari, J. Gonzalez-Jimenez, and F.-A. Moreno, “LIDAR-based autonomous navigation method for an agricultural mobile robot in strawberry greenhouse: AgriEco Robot,” ELECTRENG, vol. 6, no. 3, pp. 317–328, Jan. 2022, doi: 10.3934/electreng.2022019.
[20] Shanghai Slamtec Co., Ltd.. RPLIDAR A2 Low Cost 360 Degree Laser Range Scanner: Development Kit User Manual, 2016. Model 675 A2M4, Rev. 1.1; Accessed: Feb. 15, 2026.
[21] P. Biernacki and A. Ziebinski, “The method of reflection-based marker detection and identification to ensure accurate AGV docking,” Scientific Reports, vol. 15, no. 1, Nov. 2025, doi: 10.1038/s41598-025-25357-x.
[22] W. Wang et al., “Integrated Navigation Method for Orchard-Dosing Robot Based on LiDAR/IMU/GNSS,” Agronomy, vol. 14, no. 11, p. 2541, Oct. 2024, doi: 10.3390/agronomy14112541.
[23] Bosch Sensortec GmbH. BNO055 Intelligent 9-axis Absolute Orientation Sensor: Data Sheet, 2014. Doc. no. BST-BNO055-DS000-12, 681 Rev. 1.2; Accessed: Feb. 15, 2026
[24] P. Thavitchasri, D. Maneetham, and P. N. Crisnapati, “Intelligent Surface Recognition for Autonomous Tractors Using Ensemble Learning with BNO055 IMU Sensor Data,” Agriculture, vol. 14, no. 9, p. 1557, Sept. 2024, doi: 10.3390/agriculture14091557.
[25] M. A. B. Bhuiyan, H. M. Abdullah, S. E. Arman, S. Saminur Rahman, and K. Al Mahmud, “BananaSqueezeNet: A very fast, lightweight convolutional neural network for the diagnosis of three prominent banana leaf diseases,” Smart Agricultural Technology, vol. 4, p. 100214, Mar. 2023, doi: 10.1016/j.atech.2023.100214.
[26] A. Kakalis et al., “Farm or Lab? A Comparative Study of Oregano’s Leaf and Callus Volatile Isolates Chemistry and Cytotoxicity,” Plants, vol. 12, no. 7, p. 1472, Mar. 2023, doi: 10.3390/plants12071472.
[27] H.-T. Thai, K.-H. Le, and N. L.-T. Nguyen, “EF-CenterNet: An efficient anchor-free model for UAV-based banana leaf disease detection,” Computers and Electronics in Agriculture, vol. 231, p. 109927, Apr. 2025, doi: 10.1016/j.compag.2025.109927.
[28] A. E. B. Velasquez et al., “Reactive navigation system based on H∞ control system and LiDAR readings on corn crops,” Precision Agric, vol. 21, no. 2, pp. 349–368, June 2019, doi: 10.1007/s11119-019-09672-8.
[29] H. B. Guerrero, J. J. López A., and A. L. Jutinico, “Autonomous Mobile Robot Prototype for Vegetative Furrows Following Based on Optimal Control over ROS”, Int. J. Onl. Eng., vol. 22, no. 02, pp. pp. 4–20, Feb. 2026.
[30] H. B. Guerrero, "Development of a control system for a small-scale agricultural mobile robot for inter-row navigation," Ph.D. dissertation, School of Engineering of São Carlos, University of São Paulo, São Carlos, Brazil, 2016. doi:
https://doi.org/10.11606/T.18.2017.tde-24122016-154205
[31] H. B Guerrero, D. C. Montenegro, and A. L. Jutinico, “Reactive Fuzzy Control and Field Validation of a Low-Cost Autonomous Tracked Robot for Plantain Crop Row Following,” Eng. Res. Express, Feb. 2026, doi: 10.1088/2631-8695/ae458e.
[32] Q. Qiu et al., “Extended Ackerman Steering Principle for the coordinated movement control of a four wheel drive agricultural mobile robot,” Computers and Electronics in Agriculture, vol. 152, pp. 40–50, July 2018, doi: 10.1016/j.compag.2018.06.036.
[33] T. D. Gillespie, Fundamentals of Vehicle Dynamics. Society Of Automotive Engineers, 1992. doi: 10.4271/r-114.
[34] R. Rajamani, Vehicle Dynamics and Control. Springer Us, 2012. doi: 10.1007/978-1-4614-1433-9.
[35] I. D. Brito, M. A. Roldan Benavides, L. F. Lindarte Muñoz, A. L. Jutinico Alarcón, and H. B. Guerrero, "Small-scale car-like agricultural mobile robot for autonomous navigation in crop rows: a first ROS2 approach," in Proc. 19th Int. Congr. Electron., Control Telecommun. (CIECT XIX), Bogotá D.C., Colombia, 2024, pp. 745–769, vol. 15. ISBN: 978-958-44-5254-2. [Online]. Available: https://www.dropbox.com/scl/fi/q8pivsf65n35fxcu59m2i/Memorias-CIECT-XIX.pdf
[36] S. Macenski, A. Soragna, M. Carroll, and Z. Ge, “Impact of ROS 2 Node Composition in Robotic Systems,” IEEE Robot. Autom. Lett., vol. 8, no. 7, pp. 3996–4003, July 2023, doi: 10.1109/lra.2023.3279614.
[37] A. Bonci, F. Gaudeni, M. C. Giannini, and S. Longhi, “Robot Operating System 2 (ROS2)-Based Frameworks for Increasing Robot Autonomy: A Survey,” Applied Sciences, vol. 13, no. 23, p. 12796, Nov. 2023, doi: 10.3390/app132312796.
[38] L. Liu, Y. Liu, X. He, and W. Liu, “Precision Variable-Rate Spraying Robot by Using Single 3D LIDAR in Orchards,” Agronomy, vol. 12, no. 10, p. 2509, Oct. 2022, doi: 10.3390/agronomy12102509.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Danna C. Montenegro, Iván D. Duarte, Andrés L. Jutinico, Henry B. Guerrero
This work is licensed under a Creative Commons Attribution 4.0 International License.
