MUMBAI, India, Aug. 17 -- Intellectual Property India has published a patent application (202644088209 A) filed by C Pavani; Vandana Pranavi A M; Ranjan Raj; Pavankumar Patil; Priyanka S; Victoria Angelina Paul; Sukhada Inamdar; and Dayananda Sagar Academy Of Technology And Management on July 20, 2026, for Ai-Enabled Robot For Field Surveillance And Yield Prediction.
Inventors include Vandana Pranavi A M; Ranjan Raj; Pavankumar Patil; Priyanka S; Victoria Angelina Paul; Sukhada Inamdar; and Dayananda Sagar Academy Of Technology And Management.
The application for the patent was published on August 14, 2026, under issue no. 33/2026.
Abstract: The present invention relates to an AI-Enabled Robot for Field Surveillance and Yield Prediction, designed to automate crop monitoring, detect field anomalies, and accurately predict agricultural yield using artificial intelligence and Internet of Things (IoT) technologies. The robotic platform integrates autonomous navigation, multi-sensor data acquisition, computer vision, and machine learning to perform continuous surveillance of agricultural fields. The system collects real-time data through RGB cameras, multispectral cameras, environmental sensors, GPS, and soil sensors to assess crop health, detect diseases, identify pest infestations, monitor weed growth, and evaluate environmental conditions. An AI-based analytics engine processes the acquired data to estimate crop growth stages, predict yield, and generate actionable recommendations for irrigation, fertilization, and pest management. The robot employs obstacle avoidance and path-planning algorithms to autonomously navigate diverse field environments while transmitting collected information to a cloud platform for storage, visualization, and predictive analytics. A user-friendly dashboard provides real-time field status, crop health indices, yield forecasts, and maintenance alerts to farmers and agricultural managers. The proposed invention enhances precision agriculture by improving surveillance efficiency, reducing manual labor, enabling early detection of crop stress, optimizing resource utilization, and increasing crop productivity through intelligent and data-driven decision support.
Disclaimer: Curated by HT Syndication.
