MUMBAI, India, May 1 -- Intellectual Property India has published a patent application (202641050071 A) filed by S. Hrushikesava Raju; Viswanathan Ramasamy Reddy; Sukham Romen Singh; and Koneru Lakshmaiah Education Foundation, Mangalagiri, Andhra Pradesh, on April 20, for 'a tool for comparative analysis of machine learning and transformer-based models for crop yield prediction.'
Inventor(s) include Viswanathan Ramasamy Reddy; and Sukham Romen Singh.
The application for the patent was published on May 1, under issue no. 18/2026.
According to the abstract released by the Intellectual Property India: "Accurate yield prediction of a crop plays a vital role in enhancing agricultural productivity, resource allocation, and food security. This study presents a comparative evaluation of multiple machine learning models, including Random Forest, XGBoost, Decision Tree, Support Vector Machines, Artificial Neural Networks, and Transformer-based architectures for crop yield prediction. The proposed framework integrates heterogeneous data sources such as soil properties, weather conditions, and historical yield records. Traditional models are evaluated for their interpretability and computational efficiency, while Transformer models are assessed for their ability to capture temporal dependencies and nonlinear relationships. Experimental results demonstrate that ensemble methods like XGBoost outperform basic models in terms of accuracy, whereas Transformer-based models provide superior generalization in complex scenarios. The study highlights the strengths and limitations of each approach, offering insights into selecting appropriate models for precision agriculture applications."
Disclaimer: Curated by HT Syndication.
