MUMBAI, India, April 17 -- Intellectual Property India has published a patent application (202641043086 A) filed by G Ashwin Prabhu; R. Mathumidha; E. Jeevitha; and Dr. G. Brindha, Chennai, Tamil Nadu, on April 4, for 'agroai: voice-assisted ai-based personalized agricultural decision support system.'
Inventor(s) include R. Mathumidha; E. Jeevitha; and Dr. G. Brindha.
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 invention relates to an intelligent, automated, and machine learning-driven agricultural decision-support system designed to enhance productivity, sustainability, and profitability in farming practices. More specifically, the invention introduces a voice-assisted, personalized crop recommendation and agricultural advisory platform termed AgroAI, which integrates multi-source agricultural data, machine learning models, and multilingual voice interaction to provide real-time, location-specific, and adaptive farming recommendations. The system comprises:(a) an agricultural data acquisition module configured to collect soil parameters, weather conditions, crop history, and market trends;(b) a data preprocessing and feature engineering module configured to clean, normalize, and transform raw agricultural data into structured inputs;(c) a machine learning-based recommendation engine employing supervised algorithms such as Random Forest, Decision Tree, and K-Nearest Neighbors to generate optimized crop selection, fertilizer recommendations, and seasonal planning strategies;(d) a multilingual voice-enabled chatbot interface enabling intuitive and accessible interaction for farmers; and(e) an analytics and visualization module for real-time monitoring, insights, and reporting.Experimental evaluation demonstrates that the system achieves up to 92 percent accuracy in crop recommendation and significantly improves usability and decision-making efficiency. The invention supports adaptive learning, enabling continuous improvement based on historical data. The system is applicable to smart agriculture, precision farming, and digital agricultural ecosystems."
Disclaimer: Curated by HT Syndication.
