Various agricultural and culinary products are dried to extend their shelf lives, mostly for marine foods. In many coastal locations of the Philippines, drying fish traditionally is still practiced, although study has shown that due to weather conditions and other factors, this technique is not seen to be reliable or cost-effective. The Internet of Things (IoT)-based Direct Solar Dryer System is optimized for drying efficiency by combining a web data logger and SMS notification system using Arduino Uno and ESP-32 to address difficulties with reliability and cost effectiveness. The study focuses on the potential and system efficiency of drying Sardinella fish (Tamban) in Brgy, Calibunan, Agusan Del Norte, Philippines; as well as investigating and assessing temperature, heat index, humidity, and temperature range alert conditions using a web application portal to serve as a remote monitoring platform for dependable data visualizations. The system delivered the expected results because the direct solar drier was able to raise and maintain the requisite temperature to accelerate drying while keeping the acceptable relative humidity. Furthermore, the system's monitoring and notification capabilities, as well as effective data collecting and data display via physical and remote monitoring, are supported by SMS notifications. As a result, the effectiveness of upgrading traditional sun drying with IoT technology can help reduce the challenges and disadvantages that fish drying farmers have faced. The study, with correct drying monitoring criteria, could serve as a model for other food products that can be dried.

One of the crucial processes in the food manufacturing industry is monitoring and measuring the raw products stored in large storage structures, typically called Silos. These procedures are pivotal in dictating the industry's production, quality control, and efficiency. In this study, a wireless sensor node was developed to automatically measure the volume of the product stored in the created mock-up storage bin and a 3D surface reconstruction for visualization. The sensor node utilized a single-broad computer for its computing and wireless functionality. The sensing device comprised LiDAR sensor and a servo motor for data acquisition. The system used an open-source ROS framework to simplify the software along with its data processing pipline and hardware interface integration. The single- board and the end user devices were connected to the same network and used Remote Desktop Protocol (RDP) for remote functionality. Results from three different testing setups revealed an average error of 1.309% between the actual and predicted volume of the flour materials within the mock-up storage bin. This deviation from the actual content volume implies that the developed system offered an alternative method for efficient volume measuring of the storage bin through remote data collection and 3D visualization.

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.