MUMBAI, India, Aug. 12 -- Intellectual Property India has published a patent application (202621079655 A) filed by Dr. Abhijit Narendra Bhirud; Yamini Pitambar Warke; Jaishree Jain; R. Gokulapriya; Dr. K. Nanthitha; Dr. D. Sowjanya; Dr. Deepa Jananakumar; Dr. S. Vijay; Dr. T. Prabakaran; Dr. P. Arulkumar; Dr Guguloth Lachiram; and M. Nagatriveni on June 29, 2026, for Machine Learning-Driven Iot Framework For Real-Time Weather Prediction And Intelligent Crop Recommendation.
Inventors include Dr. Abhijit Narendra Bhirud; Yamini Pitambar Warke; Jaishree Jain; R. Gokulapriya; Dr. K. Nanthitha; Dr. D. Sowjanya; Dr. Deepa Jananakumar; Dr. S. Vijay; Dr. T. Prabakaran; Dr. P. Arulkumar; Dr Guguloth Lachiram; and M. Nagatriveni.
The application for the patent was published on August 07, 2026, under issue no. 32/2026.
Abstract: Digital technologies that assist farmers in monitoring field conditions and weather data, have enabled more wider application of such services for agriculture gaining ground. Various existing solutions measure the environment e.g. temperature, humidity, rain fall and soil moisture through connected sensors. Although these systems offer increased insight into states of the field, they often do not reach beyond raw data collection to system-frame decision-support by planners and managers. In many instances, it does not change the fact that farmers are still left to sort through a vast quantity of environmental data from scratch and decide for themselves what crops would thrive in an ever-changing climate. In addition, established weather forecasts are usually produced on the basis of big regions and do not necessarily correspond to climate at a local scale level like that within an agricultural field. This means that farming decisions remain marred with significant uncertainty. The ability to predict immediate weather has opened up entire branches of machine Tomato blight Early Special Issue Paper Writing season. The integrated framework includes environmental observation, data acquisition followed by processing and feature generation to aid machine learning for predictive analysis of the weather including crop recommendation. IoT-powered sensors feed real-time information that is ultimately converted into actionable metrics like Weather Stability Index (WSI), Soil Productivity Score (SPS), Crop Compatibility Index (CCI) and Environmental Risk Factor (ERF). A prediction engine using machine -learning analyzes these indicators to detect weather patterns, predict environmental conditions for the near or distant future, and assess crop suitability. This component also offers regular updates with information on crops to plant, alerts about environmental risks and cloud-based decision-making tools accessible via connected devices. Integrated with field-level monitoring, predictive intelligence and actionable agricultural advice, the proposed invention facilitates reduction of uncertainty in farming operations by enabling timely crop selection decisions while supporting efficient resource use practices for sustainable agricultural productivity adaptability under changing environmental conditions. FIG.1
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