IoT -Based Intelligent Environmental Control for Minimizing Spring Onion Bulb Weight Loss: A Grey-Taguchi Optimization

Abstract

Post-harvest storage of spring onion bulbs is a significant challenge for farmers in Nakhon Phanom, where environmental fluctuations lead to substantial product loss and reduced market value. This research addresses the need for a more efficient and scalable solution by integrating Internet of Things (IoT) technology with the Grey-Taguchi L9 method to optimize storage conditions, including temperature, relative humidity, and light intensity. The experimental methodology involved using nine different combinations of these environmental factors to monitor the weight loss of spring onions over a three-month period. The IoT system enabled real-time adjustments, and the optimization process utilized Grey Relational Analysis (GRA) to identify the most favorable storage conditions. The results indicate that temperature and relative humidity have the most significant effects on minimizing weight loss, with optimal conditions being a temperature of 20°C and a relative humidity of 65%, leading to the least weight loss of 5.2 grams. The model demonstrated a strong fit with a 99.74% R-squared value. Light intensity, however, had a negligible impact on weight loss. This research provides a practical solution for small-scale farmers, contributing to the advancement of post-harvest storage practices and sustainable agriculture. These findings are significant for regions with similar agricultural conditions and have the potential to reduce post-harvest losses, ultimately improving the economic stability of farming communities. Future research could explore applying this IoT-based approach to other crops and integrating predictive analytics for further system improvements.