Document Type : Primary Research paper
Assistant Professor, Dept. of EEE, AMET Deemed to be University, Chennai
Assistant Professor, Dept. of EEE , Sri Sairam Engineering College, Chennai
Professor, Dept. of EEE, AMET Deemed to be University, Chennai
The practice of dividing materials into distinct groups based on the constituent material of the thing under examination is known as material recognition. It is a major issue in a variety of disciplines, particularly in the industrial sector. Separating materials and packaging different materials is one stage in industries that employ production lines to make their products. A number of small and medium-sized businesses who can't afford full automation rely on manual data collecting and compilation to generate reports. Inconsistencies and inaccuracies are common in manual data processing. This leads to minor growth of such industries in the competitive market. Low cost automation using Inter of Things (IoT) is one solution especially for medium and small scale industries. An alternative to human data handling is an IoT-based automatic data collecting and processing system . This paper discusses the advantages of using an automated data gathering and display system to save money, time, and effort. It enhances the accuracy of reports for management greatly. We applied deep learning to a water can product system and put it through its paces on the Flicker Materials Database (FMD). The system is self-contained, cost-effective, and precise, and it can be installed in any factory with few alterations and no loss of quality.