MUMBAI, India, June 30 -- Intellectual Property India has published a patent application (202641066557 A) filed by Rmk Engineering College on May 27, 2026, for A Graph Neural Network Driven Advanced Persistent Threat Detection System With Real-Time Encrypted Telemetry Analysis.
Inventors include Dr Sandra Johnson; Dr. P. Shobha Rani; Dr. S Srijayanthi; Dr. Gladiss Merlin N. R; and Dr Shanthi M.
The application for the patent was published on June 26, 2026, under issue no. 26/2026.
Abstract: Agricultural productivity is significantly affected by pest infestations that often remain undetected during early stages using conventional monitoring methods. The present invention proposes a multi-spectral drone image processing system for early pest detection integrated with a distributed ledger-based farm data integrity verification mechanism. The system utilizes an unmanned aerial vehicle equipped with multi-spectral sensors to capture high-resolution crop imagery across visible and near-infrared spectral bands. These images provide detailed spectral information about crop health conditions that cannot be observed through traditional visual inspection methods. The drone operates autonomously through a flight planning and navigation module that enables systematic scanning of agricultural fields for real- time crop monitoring. The captured multi-spectral images are transmitted to an image processing unit where advanced algorithms analyze vegetation indices and spectral signatures to identify abnormal crop patterns associated with pest infestations. Machine learning-based pest detection models evaluate crop stress indicators and generate pest detection results including infestation location and severity. The processed data is then transmitted to a cloud computing environment for storage, analysis, and visualization. Farmers and agricultural experts can access this information through a user interface module that provides crop health dashboards, pest alerts, and decision-support reports to enable timely pest management and improved agricultural productivity. To ensure the reliability and authenticity of farm monitoring data, the invention integrates a distributed ledger network that securely records agricultural monitoring transactions generated by the system. The processed data, including pest detection results, drone flight information, and geo-referenced field data, is converted into encrypted blockchain transactions and verified through participant nodes using consensus mechanisms. Once validated, the information is permanently stored in the distributed ledger to provide tamper-proof data integrity, transparency, and traceability. This integrated approach enhances precision agriculture by combining drone-based remote sensing, intelligent image processing, and secure data management for efficient and trustworthy farm monitoring. Keywords Multi-spectral drone, pest detection, precision agriculture, vegetation indices, image processing, distributed ledger, blockchain, crop health monitoring, remote sensing, farm data integrity.
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