UTILIZATION OF ROVER AI AGENTS FOR PALM OIL PLANTATION AUTOMATION

Aryanto Aryanto; Novia Utami Putri; I Nengah Marccel Janara Brata

doi:10.37058/jeee.v7i1.13033

UTILIZATION OF ROVER AI AGENTS FOR PALM OIL PLANTATION AUTOMATION

Aryanto Aryanto, Novia Utami Putri, I Nengah Marccel Janara Brata

Abstract

ABSTRACT: The integration of artificial intelligence into autonomous rover systems represents a paradigm shift in how palm oil plantations can be managed and operated. Our research explores the deployment of intelligent rover agents that combine sophisticated machine learning algorithms with advanced robotics to transform traditional agricultural practices. Through extensive field trials spanning 500 hectares of operational plantations, we observed remarkable improvements in disease detection accuracy, reaching 94 percent. At the same time, pesticide consumption decreased by 87 percent through the use of precision application techniques. The system architecture leverages edge computing capabilities to process multispectral imagery and environmental sensor data in real-time, enabling an immediate response to detected anomalies. Deep reinforcement learning guides the navigation system, allowing the rovers to autonomously traverse complex plantation terrain, while convolutional neural networks analyze plant health indicators with unprecedented precision. Communication between multiple rover units occurs through a federated learning framework that preserves bandwidth and enables collective intelligence growth without compromising data privacy. This comprehensive approach yielded a 35% increase in overall operational efficiency, underscoring the transformative potential of AI-driven automation in tropical agricultural environments.

KEYWORDS: AI agents, autonomous rovers, deep learning, edge computing, palm oil automation, precision agriculture

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References

N. Khan, M. A. Kamaruddin, U. U. Sheikh, Y. Yusup, and M. P. Bakht, "Oil palm and machine learning: Reviewing one decade of ideas, innovations, applications, and gaps," Agriculture, vol. 11, no. 9, p. 832, 2021.

K. Kipli et al., "Deep learning applications for oil palm tree detection and counting," Smart Agricultural Technology, vol. 5, p. 100241, 2023.

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, p. 52, 2021.

M. N. Akhtar, E. Ansari, S. S. M. Alhady, and E. A. Bakar, "Leveraging on advanced remote sensing- and artificial intelligence-based technologies to manage palm oil plantation for current global scenario: A review," Agriculture, vol. 13, no. 2, p. 504, 2023.

S. Fountas et al., "Agricultural robotics for field operations," Sensors, vol. 20, no. 9, p. 2672, 2020.

H. P. Wibowo, I. S. Sitanggang, Mushthofa, and H. A. Adrianto, "Large-scale oil palm trees detection from high-resolution remote sensing images using deep learning," Big Data and Cognitive Computing, vol. 6, no. 3, p. 89, 2022.

M. A. Azizan, A. M. Ibrahim, and I. A. H. M. Asming, "Processing and classification of landsat and sentinel images for oil palm plantation detection," Remote Sensing Applications: Society and Environment, vol. 26, p. 100747, 2022.

N. S. N. Shaharum et al., "Oil palm mapping over Peninsular Malaysia using Google Earth Engine and machine learning algorithms," Remote Sensing Applications: Society and Environment, vol. 17, p. 100287, 2020.

J. Su, X. Zhu, S. Li, and W.-H. Chen, "AI meets UAVs: A survey on AI empowered UAV perception systems for precision agriculture," Neurocomputing, vol. 518, pp. 242-270, 2023.

A. Descals et al., "High-resolution global map of smallholder and industrial closed-canopy oil palm plantations," Earth System Science Data, vol. 13, no. 3, pp. 1211-1231, 2021.

A. Sharma, A. Jain, P. Gupta, and V. Chowdary, "Machine learning applications for precision agriculture: A comprehensive review," IEEE Access, vol. 9, pp. 4843-4873, 2021.

R. Xu and C. Li, "A modular agricultural robotic system (MARS) for precision farming: Concept and implementation," Journal of Field Robotics, vol. 39, no. 6, pp. 804-822, 2022.

M. T. Linaza et al., "Data-driven artificial intelligence applications for sustainable precision agriculture," Agronomy, vol. 11, no. 6, p. 1227, 2021.

N. N. Thilakarathne, M. S. A. Bakar, P. E. Abas, and H. Yassin, "A cloud enabled crop recommendation platform for machine learning-driven precision farming," Sensors, vol. 22, no. 16, p. 6299, 2022.

J. Yang, J. Ni, Y. Li, J. Wen, and D. Chen, "The intelligent path planning system of agricultural robot via reinforcement learning," Sensors, vol. 22, no. 12, p. 4316, 2022.

E. A. Abioye et al., "Precision irrigation management using machine learning and digital farming solutions," AgriEngineering, vol. 4, no. 1, pp. 70-103, 2022.

K. Xu et al., "A new machine learning approach in detecting the oil palm plantations using remote sensing data," Remote Sensing, vol. 13, no. 2, p. 236, 2021.

H. Zhou, X. Wang, W. Au, H. Kang, and C. Chen, "Intelligent robots for fruit harvesting: Recent developments and future challenges," Precision Agriculture, vol. 23, no. 5, pp. 1856-1907, 2022.

P. Ahmadi, S. Mansor, B. Farjad, and E. Ghaderpour, "Unmanned aerial vehicle (UAV)-based remote sensing for early-stage detection of Ganoderma," Remote Sensing, vol. 14, no. 5, p. 1239, 2022.

I. A. Zualkernan et al., "Machine learning for precision agriculture using imagery from unmanned aerial vehicles (UAVs): A survey," Drones, vol. 7, no. 6, p. 382, 2023.

DOI: https://doi.org/10.37058/jeee.v7i1.13033