Real-Time Framework for Sustainable IoT-Based Grain Drying Integrated Load, Temperature, and Energy Performance Monitoring

This research develops an Internet of Things (IoT)-based real-time framework for integrated monitoring of load, temperature, and energy consumption performance in grain drying.Although IoT technology has transformed the agricultural sector, implementing low-cost sensors for precision monitoring in grain drying and the relationship between monitoring data and energy optimization still faces various challenges.The study integrated the DHT11 sensor and HX711 load cell with a closed-loop system and a mathematical model for estimating moisture content.The experimental method used comprehensive observation instruments, including continuous load measurement and monitoring at five strategic points of the DHT11 sensor and real-time power measurement.The results showed an increase in energy efficiency of 27.5% and reduced drying time by 20-30% compared to conventional farmers' methods in Merauke.In addition, the system can achieve an optimal humidity level (12-14%).The consistency of product quality reached 96.7%, and seed cracking cases reduced by 15.7%.The system ensures moisture accuracy, achieving an average error percentage of 1.92% in moisture detection and an overall drying efficiency of 67.1%; using a blower improves on the results of previous studies, which only reached 64.8% using LPG.The framework provides theoretical contributions in the form of empirical validation of drying dynamics correlation models and energy optimization, as well as practical contributions in the form of sustainable drying solutions that are adaptable to various scales of operations and support improving global food security through improved post-harvest efficiency.