MUMBAI, India, May 1 -- Intellectual Property India has published a patent application (202641049198 A) filed by Srm Institute Of Science And Technology, Ramapuram Campus; and Easwari Engineering College, Chennai, Tamil Nadu, on April 17, for 'smart agriculture systems: a machine learning and iot approach to sustainable farming.'
Inventor(s) include Gokulpriyan K; Dharani P; Kishore P; C. Kudiyarasudevi; and S. Kamaleswari.
The application for the patent was published on May 1, under issue no. 18/2026.
According to the abstract released by the Intellectual Property India: "This invention discloses a federated multi-agent reinforcement learning (Fed-MARL) system configured to enable autonomous coordination of agricultural drone swarms across multiple geographically distributed farms without sharing raw sensor data, crop health information, or operational parameters. The system performs Byzantine-robust model aggregation, QMIX-based cooperative policy learning, agriculture-aware reward optimization, and differential privacy integration to ensure that multi-drone coordination policies are collaboratively optimized while maintaining data sovereignty, operational security, and adversarial resilience. The disclosed architecture enables systematic crop monitoring, precision chemical application, stress detection, and energy-efficient path planning through coordinated multi-agent behavior learned from heterogeneous agricultural environments. Machine learning agents operate through monotonic value function factorization that guarantees decentralized execution consistency while supporting centralized training across federated farm participants. Unlike conventional single-drone precision agriculture systems that operate independently without knowledge sharing, or centralized multi-farm platforms that compromise farmer data privacy, the present invention establishes a privacy-preserving collaborative intelligence substrate capable of maintaining Byzantine fault tolerance, differential privacy guarantees, edge-deployable inference, and verifiable convergence stability. This enables transparent, auditable, and agriculturally optimized autonomous drone swarm operations across cooperative farming networks while protecting sensitive agricultural data from reconstruction attacks or malicious participant influence."
Disclaimer: Curated by HT Syndication.
