MUMBAI, India, Aug. 10 -- Intellectual Property India has published a patent application (202641083250 A) filed by Saveetha Institute Of Medical And Technical Sciences on July 07, 2026, for Ai - Based Real - Time Crop Image Analystics And Crop Insurance System.
Inventors include Siga Mahesh; Dr. A. Moorthy; and Dr Ramya Mohan.
The application for the patent was published on July 31, 2026, under issue no. 31/2026.
Abstract: The growing dependence of farmers on traditional crop assessment and insurance claim processes has revealed significant challenges, particularly in rural and remote regions. Manual crop inspection methods are time-consuming, subjective, and often delayed due to the limited availability of agricultural officers, infrequent field visits, and inconsistent damage evaluation standards. As a result, farmers frequently experience delayed insurance settlements, inadequate compensation, and a reduced level of trust in crop insurance systems. Conventional digital solutions, when available, often rely on static data entry or manual image review, providing minimal real-time insights or automated decision support. These systems fail to provide farmers with accurate, transparent, and timely feedback on crop health, pest infestations, or climate-induced damage. Consequently, critical gaps remain in effective risk assessment, loss estimation, and insurance claim processing. To address these challenges, the proposed system leverages Al-based real-time crop image analytics to enable instant and objective assessment of crop conditions using images captured through a mobile application. By employing advanced image processing techniques and machine learning models, the system accurately detects crop diseases, pest infestations, growth abnormalities, and the severity of damage. This data-driven approach significantly reduces human bias, enhances evaluation consistency, and improves the reliability of insurance claim verification. Beyond crop analytics, the platform empowers farmers and insurance stakeholders through features such as automated loss estimation, Al-assisted claim recommendations, real-time monitoring insights, and geo-tagged image validation. Additionally, the system supports offline image capture with delayed synchronization, ensuring functionality in low-connectivity rural regions. Overall, the proposed solution aims to improve transparency, accelerate insurance settlements, and restore farmer confidence in crop insurance mechanisms through intelligent automation.
Disclaimer: Curated by HT Syndication.
