MUMBAI, India, April 17 -- Intellectual Property India has published a patent application (202531097611 A) filed by Narula Institute Of Technology, Kolkata, West Bengal, on Oct. 9, 2025, for 'machine learning-based framework for forecasting agricultural yield influenced by dynamic weather conditions.'

Inventor(s) include Aman Kumar; Nikhil Kumar Singh; Debrupa Pal; Dr. Papri Ghosh; Dr. Subhram Das; and Dr. Md. Ashifuddin Mondal.

The application for the patent was published on April 17, under issue no. 16/2026.

According to the abstract released by the Intellectual Property India: "The present discloses a novel system and method that utilizes advanced machine learning techniques to accurately forecast crop yields under varying climatic scenarios. The invention integrates diverse data sources, including historical weather records, soil profiles, satellite imagery, and crop yield statistics, to train predictive models capable of capturing complex, non-linear interactions between environmental variables and crop performance. The system architecture comprises a multi-stage pipeline involving data collection, preprocessing, feature engineering, model training, and predictive analysis. Machine learning algorithms such as Random Forest, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Gradient Boosting Machines are employed to develop robust models that can generalize well across different regions and crop types. The framework not only forecasts expected agricultural yield but also identifies climate-stressed zones and suggests adaptive strategies such as suitable crop variety selection, optimized sowing schedules, and water resource planning. The invention is intended to assist farmers, agricultural planners, and policymakers in making informed decisions, ultimately improving food security, resource efficiency, and resilience to climate change. This system offers a scalable, data-driven approach to modern agricultural management in the face of increasing climatic unpredictability."

Disclaimer: Curated by HT Syndication.