MUMBAI, India, Feb. 13 -- Intellectual Property India has published a patent application (202611002092 A) filed by Dr. Satyajee Srivastava; Dr. Syed Irfan Ali; Dr. J. Ruby Elizabeth; Dr Mohammad Aamir Almas; Dr. Dhiraj Kumar Yadav; Mrs. Sangeeta Uranakar; and Dayananda Sagar Academy Of Technology And Management, Faridabad, Haryana, on Jan. 8, for 'iot-integrated machine learning system for automated soil classification and intelligent fertilizer recommendation to enhance crop yield.'

Inventor(s) include Dr. Satyajee Srivastava; Dr. Syed Irfan Ali; Dr. J. Ruby Elizabeth; Dr Mohammad Aamir Almas; Dr. Dhiraj Kumar Yadav; and Mrs. Sangeeta Uranakar.

The application for the patent was published on Feb. 13, under issue no. 07/2026.

According to the abstract released by the Intellectual Property India: "The present invention relates to the development of an IoT-integrated machine learning (ML) solution developed to automatically classify soil types and provide intelligent fertilizer suggestions to enhance crop production in precision agriculture. This solution involves the deployment of soil sensors such as moisture sensors, pH sensors, nutrient sensors (N-P-K), temperature sensors, and hyperspectral image sensors using low-power wide-area networks (LoRaWAN) to extract real-time data from agricultural lands. Based on the multi-data type, it involves data preprocessing followed by feature engineering using classifiers designed using hybrid CNN-LSTM networks that provide over 95% classification achievement in classifying various types of soil in multiple agricultural climatic regions. To further enhance crop production by proper nutrient delivery using optimal nutrient formulation, it involves the use of reinforcement learning agents that simulate crop growth processes by considering various weather forecasts to decrease the use of nutrients by 25-30% with an increment in crop production by up to 18%. Nowadays, being portable using edge computing/intelligence on various smart devices purpose-built to be energy-efficient to be widely used by farmers using mobile stations/supporting dashboards maximizes scalability in agricultural production processes to support sustained agricultural production strategies in changing climatic conditions."

Disclaimer: Curated by HT Syndication.