Intellectual Property India Publishes Patent Application for Machine Learning-Based Mushroom Growth Prediction And Smart Harvesting System

MUMBAI, India, June 26 -- Intellectual Property India has published a patent application (202641067435 A) filed by Dr. Krishna Prasad S; Sakshi Dubey; Dr. Sunanda Prabhakar Khandait; S. Dharanika; Dr. Prabhakar D. Khandait; Dr. Atul Namdeo Wagh; Dr Suniti Kumar Kuriyal; Dr. Aditya; Dr. Apurba Das; Urlam Pranita; Venkata Satya Praveen Rani; and Nancy Sharma on May 29, 2026, for Machine Learning-Based Mushroom Growth Prediction And Smart Harvesting System.

Inventors include Dr. Krishna Prasad S; Sakshi Dubey; Dr. Sunanda Prabhakar Khandait; S. Dharanika; Dr. Prabhakar D. Khandait; Dr. Atul Namdeo Wagh; Dr Suniti Kumar Kuriyal; Dr. Aditya; Dr. Apurba Das; Urlam Pranita; Venkata Satya Praveen Rani; and Nancy Sharma.

The application for the patent was published on June 19, 2026, under issue no. 25/2026.

Abstract: Machine Learning-Based Mushroom Growth Prediction and Smart Harvesting System is the proposed invention. The proposed invention focuses on machine learning- based mushroom growth prediction and smart harvesting system using the Extreme Gradient Boosting (XGBoost) algorithm for intelligent cultivation management. The proposed framework integrates IoT-enabled environmental sensors, image acquisition systems, and predictive analytics to continuously monitor critical cultivation parameters including temperature, humidity, carbon dioxide concentration, substrate moisture, airflow, and mushroom growth characteristics. The collected data are processed and analysed using the XGBoost model to predict mushroom growth stages, estimate yield quality, identify contamination risks, and determine the optimal harvesting period with high accuracy. The system further generates adaptive recommendations for environmental control, irrigation scheduling, ventilation management, and harvesting operations to maintain favourable growth conditions and improve production efficiency. Real-time monitoring and automated predictive analysis reduce manual intervention, minimize resource wastage, and enhance crop quality and yield consistency. The proposed intelligent framework supports sustainable smart agriculture by enabling data-driven mushroom cultivation and efficient harvesting optimization in commercial farming environments.

Disclaimer: Curated by HT Syndication.