Intelligent Rover for Real-time Coconut Disease Detection and Precision Fertilizer Application
Keywords:
Autonomous rovers, Computer vision, Deep learning, Disease detection, Precision agriculture, YOLOv8Abstract
Modern agriculture faces significant challenges in early disease detection and efficient resource management. This study presents an autonomous mobile platform integrating computer vision and deep learning for real-time coconut tree health assessment. The proposed system employs the YOLOv8 architecture for disease identification and growth stage classification, achieving detection rates exceeding 90% in field trials. A Raspberry Pi-based processing unit coordinates with Arduino microcontrollers to enable remote operation through a web-based interface. The platform captures live imagery, performs on-device inference, and provides fertilizer recommendations based on detected conditions. Field validation demonstrates the system’s capability to reduce manual inspection time by 75% while maintaining detection accuracy comparable to expert assessment. The integration of autonomous navigation with precision agriculture techniques offers a scalable solution for plantation monitoring and targeted intervention. The rover system identifies four critical disease classes, including Bud Rot, Stem Bleeding, Grey Leaf Spot, and Bud Dropping, with an overall accuracy of 93.1%. Real-time processing operates at 26 frames per second with minimal latency, enabling smooth video streaming to a Flutter-based mobile application. The system’s distributed architecture separates high-level AI processing on Raspberry Pi from time-critical motor control on Arduino, ensuring reliable operation. Battery-powered operation provides 4.5 hours of continuous monitoring with WiFi connectivity extending up to 50 meters. The automated fertilizer recommendation engine achieves 100% alignment with expert agricultural protocols, supporting sustainable farming practices through optimized chemical application. This cost-effective implementation using commercially available components makes precision agriculture technology accessible to small and medium-scale farmers, addressing critical labor shortages while improving crop health management.
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