Work

Image Histogram Analysis in a Webcam-Based Non-Ionizing Tomographic System for Early Shrimp Disease Detection

Year: 2026

Type: article

Abstract: This study presents the development of a non-ionizing tomography imaging system using a webcam to detect diabetes-like metabolic disorders in Litopenaeus vannamei through digital image histogram analysis. The method employs controlled glucose injections (0–10 mL) to simulate metabolic variation, followed by optical image acquisition and RGB histogram characterization using Python-based GUI software. Calibration with a white-screen baseline ensures consistent optical response. Results show significant RGB histogram shifts correlating with glucose concentration changes, indicating optical density variations in shrimp tissues. The RGB mean intensity decreased by approximately 20–35% between 0 and 6 mL glucose concentration. Linear regression analysis within the 0–6 mL range yielded a sensitivity of 11.48 intensity units/mL with R² = 0.987, and an estimated detection limit of 0.4 mL equivalent glucose change. The method demonstrates potential as a non-invasive, low-cost diagnostic tool for early metabolic disorder detection in aquaculture. Keywords - non-ionizing tomography, RGB histogram analysis, Litopenaeus vannamei, glucose detection, digital image processing

Source: Techno Com

DOI: 10.62411/tc.v25i3.16203