MUMBAI, India, May 29 -- Intellectual Property India has published a patent application (202631060762 A) filed by Aryan Singh; Dr. Manish Pratap Singh; Dr. Hari Om; Ankit Singh; Avdhesh Kumar; Soumya Shrivastava; Rishi Agrawal; and Tejas Singh, Dhanbad, Jharkhand, on May 13, for 'system and method for data-driven soil moisture prediction using satellite-derived geophysical data and gradient boosting machine learning models.'
Inventor(s) include Aryan Singh; Dr. Manish Pratap Singh; Dr. Hari Om; Ankit Singh; Avdhesh Kumar; Soumya Shrivastava; Rishi Agrawal; and Tejas Singh.
The application for the patent was published on May 29, under issue no. 22/2026.
According to the abstract released by the Intellectual Property India: "The present disclosure relates to a system and method for data-driven soil moisture prediction using satellite-derived geophysical data and gradient boosting machine learning models. The system acquires multi-source geospatial data including synthetic aperture radar (SAR), optical imagery, thermal data, vegetation indices, terrain attributes, and climatic parameters. The data is pre-processed through noise filtering, normalization, and spatial-temporal alignment to ensure accuracy and consistency. Relevant geophysical features are extracted and provided to a hybrid gradient boosting framework configured to model complex non-linear relationships and spatio-temporal dependencies. An adaptive feature fusion mechanism dynamically weights heterogeneous inputs based on quality and relevance, thereby improving prediction robustness. The system generates soil moisture levels along with confidence metrics, actionable recommendations, and explainable outputs. Further, continuous learning mechanisms enable model adaptation using historical and real-time data. The disclosed invention enhances prediction accuracy, scalability, and reliability, thereby supporting precision agriculture, optimized irrigation management, and improved resource utilization."
Disclaimer: Curated by HT Syndication.
