MUMBAI, India, Aug. 24 -- Intellectual Property India has published a patent application (202641097560 A) filed by Adichunchanagiri University; and Bgs Institute Of Technology on August 12, 2026, for A Smart Garbage Content Estimation And Prediction System And Method.
Inventors include Anilkumar Kelegere Basavaraju; Dr. Siddartha Bommenahalli Krishnegowda; Dr. Naveen Nanjappa; and Mrs. Nischitha Agalaya Ramesh.
The application for the patent was published on August 21, 2026, under issue no. 34/2026.
Abstract: Title: A Smart Garbage Content Estimation and Prediction System and Method. The present invention discloses a garbage content estimation and prediction system (100) using the Internet of Things (IoT) and machine learning for intelligent waste management as shown in fig 1. The system comprises an IoT sensor module (110) including an infrared sensor, a moisture sensor, a load cell, and an ultrasonic sensor for detecting waste presence, identifying waste type, measuring waste weight, and determining garbage fill level, respectively. A data acquisition module (120) comprising a servo motor and a microcontroller receives and preprocesses the sensor data while automatically segregating wet and dry waste. A data transfer unit (130) transmits the processed data to a cloud platform through a wireless communication network. An ML-based prediction unit (140) employs a linear regression model to analyse historical and real-time sensor data for predicting future garbage fill levels, garbage accumulation trends, and estimated overflow time. A smart response and guidance module (150) generates waste collection alerts and operational recommendations, while an administration dashboard and logging module (160) displays real-time monitoring information and stores historical records and prediction logs. The invention is applicable to smart cities, municipalities, residential communities, hospitals, educational institutions, industries, and commercial facilities for predictive waste management. Experimental evaluation demonstrates improved monitoring accuracy, automated waste segregation, optimized waste collection scheduling, approximately 30% reduction in collection frequency, reduced operational costs, and prevention of garbage bin overflow, thereby enhancing environmental sustainability and resource utilization.
Disclaimer: Curated by HT Syndication.
