<![CDATA[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]]>
Copyrights © 2025
