MUMBAI, India, Oct. 26 -- Intellectual Property India has published a patent application (202641122945 A) filed by Ms. Joice Jesie M; Ms. Deepa A; Ms. Punithasurya K; and S. A. Engineering College on October 14, 2026, for Machine Learning Framework For Rice Production Forecasting And Supplier Risk Assessment.
Inventors include Ms. Joice Jesie M; Ms. Deepa A; and Ms. Punithasurya K.
The application for the patent was published on October 23, 2026, under issue no. 43/2026.
Abstract: The rice production and supply chain involves multiple stakeholders such as farmers, suppliers, rice mills, and distributors. Inefficiencies in supplier reliability and inaccurate production forecasting often lead to supply shortages, overstocking, increased operational costs, and reduced productivity. To address these challenges, this project proposes a Smart Rice Supplier Reliability and Production Forecast System that enhances supply chain management in rice production and distribution. The proposed system utilizes Long Short-Term Memory (LSTM), a deep learning model capable of analysing time-series data, to predict future rice production and supplier performance based on historical supply data, seasonal patterns, and demand trends. By learning from past supplier behaviour and production records, the system can forecast potential supply fluctuations and identify reliable suppliers more accurately. In addition, cloud-based classification techniques are integrated to manage and process large-scale supplier and production data efficiently. The cloud infrastructure enables real-time data storage, analysis, and accessibility, ensuring scalability and improved decision-making for stakeholders. The classification model helps categorize suppliers based on reliability factors such as delivery consistency, quality of supply, and historical performance. By combining LSTM-based forecasting with cloud classification, the system provides a smart and automated solution for monitoring supplier reliability and predicting rice production levels. This helps rice mills and supply chain managers make informed decisions, optimize procurement strategies, reduce risks in the supply chain, and improve overall efficiency in rice production and distribution. The proposed solution ultimately contributes to a more reliable, data-driven, and sustainable rice supply chain management system.
Disclaimer: Curated by HT Syndication.
