MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641107459 A) filed by Manivannan K; M. Benila; D. Vengaimarbhan; S. M. Sobana; R Sulaiman; K. Nithiya; and V. S. B. Engineering College, Karur on September 07, 2026, for Autonomous Agricultural Robot With Multimodal Crop Disease Detection, Plant Row Navigation And Targeted Disease Localization.
Inventors include Manivannan K; M. Benila; D. Vengaimarbhan; S. M. Sobana; R Sulaiman; K. Nithiya; and V. S. B. Engineering College, Karur.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: The present invention relates to an autonomous agricultural robotic system for detecting and localizing diseases in crops using multimodal sensing, autonomous plant- row navigation, and artificial-intelligence-based image analysis. The system comprises a mobile robotic platform, a navigation subsystem, a multimodal sensing subsystem, an embedded processing unit, a disease detection module, a disease localization module, and a wireless communication module. The multimodal sensing subsystem acquires crop information using one or more RGB cameras, depth sensors, and optionally thermal or multispectral sensors. The navigation subsystem autonomously guides the robotic platform through crop rows using positioning information, crop-row detection, inertial measurements, wheel-encoder data, and obstacle detection. The acquired crop data is processed by an artificial-intelligence model to identify disease-related characteristics such as lesions, spots, discoloration, wilting, abnormal texture, or other symptoms. A confidence-based verification mechanism enables additional image acquisition or sensor repositioning when a disease detection result is uncertain. The detected disease is associated with a geographic or field-relative position and an estimated severity level to generate a targeted crop disease distribution map. The system enables continuous and location-specific crop monitoring, early disease identification, reduced manual inspection, and improved precision in subsequent agricultural management operations
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