Grain Quality Analysis Using CNN And Iot Based Strategic Safeguarding - Pullagura, Joshua Reginald; Rao, A. V. Nageswara; Kumar, P. Sundara; Raju, M.V.; Madhavi, B. (Metallurgical and Materials Engineering, 2025)
More than half of the global population consumes rice daily, making it a staple that fulfills over 21% of the world’s caloric needs—more than any other food. The demand for rice is highest when its quality is optimal. Traditionally, the type and quality of rice are assessed visually by human inspectors. However, this method is labor-intensive, time-consuming, relies on human expertise, and is subject to inconsistencies due to the inspector’s physical condition. To overcome these limitations, this work proposes an automated system that leverages digital image processing techniques for the identification and classification of rice grains. Image processing offers a non-contact, efficient, and reliable alternative by capturing images of the grains for analysis. Using MATLAB, the system preprocesses the images, segments the rice grains, and extracts relevant features. The endpoints of each grain are identified to calculate their length and breadth, which are key parameters in determining grain quality. Additionally, the rice grains are stored in containers equipped with DHT sensors that continuously monitor and record temperature and moisture levels to ensure proper preservation conditions.