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Patent granted for highly efficient ‘Drought Assessment System & Tool’

By Lokmat English Desk | Updated: April 27, 2025 19:45 IST

Chhatrapati Sambhajinagar: The patent office of the Government of India granted two city researchers a patent on an ‘Agriculture ...

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Chhatrapati Sambhajinagar: The patent office of the Government of India granted two city researchers a patent on an ‘Agriculture Drought Assessment System and Tool Based Geospatial Data’.

Dr Sandeep Gaikwad and Dr K V Kale (vice-chancellor, Dr Babasaheb Ambedkar Technological University (Bamu) addressing the urgent need for precise and real-time drought assessment, especially for drought-prone regions like Marathwada, which faces drought episodes every 2–3 years, developed an innovative system called ‘Drought Monitoring and Assessment that combines Geospatial Technology, Internet of Things (IoT), and Artificial Intelligence (AI).

The researchers said that traditional drought assessment methods such as Annewari and Paisewari, though historically significant, have major limitations compared to modern technologies.

The methods are based on subjective assessments, relying heavily on visual inspections and crop-cutting experiments, which are prone to human error.

They said that the newly patented methodology uniquely integrates satellite remote sensing data, smartphone-based ground truth collection, and IoT sensor networks to monitor agricultural drought conditions with high accuracy. “A pilot study was conducted in Vaijapur tehsil of Chhatrapati Sambhajinagar district, a region highly vulnerable to drought,” they said.

The entire research and development work was meticulously carried out by Dr. Sandeep Gaikwad, under the valuable guidance of Dr K V Kale. In this patent, Dr. Amol Vibhute, Dr. Rajesh Dhumal and Dr Rupali Surase also contributed significantly to the research work.

The system collects and integrates data from IoT devices, satellite data archives, and ground truth data collected through a dedicated mobile application. Advanced Machine Learning and Deep Learning algorithms are applied to satellite data for monitoring and ssessment. The system computes time-series vegetation to detect early signs of drought stress. It then generates classified drought maps and displays them on a specially developed Geospatial portal (DroughtGIS), making the data easily accessible to farmers, researchers, and decision-makers.

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