Published Online:May 2025
Product Name:The IUP Journal of Mechanical Engineering
Product Type:Article
Product Code:IJME030525
DOI:10.71329/IUPJMECH/2025.18.2.41-49
Author Name:V Nevedha, K Gajalakshmi and S Saravanan
Availability:YES
Subject/Domain:Engineering
Download Format:PDF
Pages:41-49
Ensuring the quality of stored rice is vital for consumer satisfaction and food safety. The detection and quantification of insects in stored rice is critical in determining the quality of rice, which dictates the cost of the rice. An attempt is made to develop an automated system, based on Internet of Things (IoT), for identifying and counting rice weevil—Sitophilus oryzae—in stored grains, employing thresholding-based segmentation and object recognition. The insects and rice grains are differentiated by analyzing texture, color, and shape in a comprehensive database of 600 varieties of rice images in a Python environment. Thresholding segmentation effectively isolates potential insect regions, while thresholding-based object recognition algorithm classifies and quantifies the insects. The proposed model identifies the insects in the input images with 98% accuracy, signifying its consistency and competence. The IoT-based model reduces manual inspection and paves the way for automated inspection in large-scale rice storage locations.
As the world population is expected to reach 9.1 billion by 2050, food security of the people is critical (Rahut et al., 2022). Despite the rise in food demand, the percentage of cultivatable land and natural resources continue to decline.