MUMBAI, India, Aug. 31 -- Intellectual Property India has published a patent application (202641101050 A) filed by Sri Eshwar College Of Engineering on August 20, 2026, for Data-Driven Palm Oil Yield Prediction Using Hybrid Machine Learning And Deep Learning On Multi- Source Agricultural Data.
Inventor includes Mr. K. P. Siva.
The application for the patent was published on August 28, 2026, under issue no. 35/2026.
Abstract: Accurate prediction of palm oil yield is essential for effective agricultural planning and sustainable resource management. However, traditional yield forecasting methods based on manual estimation and static statistical models often fail to capture the seasonal variability and dynamic patterns present in agricultural data. This study presents an integrated predictive framework that combines machine learning and deep learning techniques to improve palm oil yield prediction accuracy. Multi-year datasets comprising climatic variables, rainfall, soil properties, fertilizer usage history, and plantation age were collected and processed using feature engineering techniques. Several machine learning models were evaluated alongside a Long Short-Term Memory (LSTM) network to capture nonlinear relationships and temporal dependencies inherent in agricultural data. Model performance was assessed using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Experimental results indicate that ensemble and deep learning models significantly outperform traditional approaches in yield prediction accuracy. The proposed predictive system provides valuable insights that can assist plantation managers in optimizing harvest scheduling, improving resource utilization, and supporting sustainable agricultural practices.
Disclaimer: Curated by HT Syndication.
