Melody Mae Maluya

Adequate monitoring of flour storage bins in the food manufacturing industry can prevent profit loss from underproduction and overstocking. Manual volume measurement is labor-intensive and error-prone. With the need for efficient monitoring in mind, this study presents the design and development of volumetric measurement of the flour inside a storage bin using 2D-based rotating LiDAR to capture 3D point cloud data. The proposed system eliminates manual probing by fully automating the scanning and volumetric computation workflow. Instead of relying on discrete physical measurements inside the bin, the 2D rotating LiDAR continuously captures the interior walls and flour surface to generate a dense 3D point cloud. This removes the need for operators to insert rods or probes and thereby avoids human-induced measurement variability. Furthermore, because the system computes flour volume directly from geometric reconstruction rather than converting probe depths using a uniform surface assumption, it does not rely on a constant material density and is therefore more robust to compaction differences within the bin. The high-resolution point cloud also generates accurate mapping of non-uniform and irregular surface geometries, which captures true depressions, peaks, and sloped regions that manual methods typically miss. A dedicated web application was developed to send commands to the system for automated scanning and real-time volume computation. Successful real-world testing showed the system’s reliability, with an accuracy level of 1.013 ± 0.70% MAPE across varied flour quantities and surface contours.