Predictive Digital Twin Modeling for Quality Preservation of Perishable Agricultural Products during Storage

Rustan (Universitas Sulawesi Tenggara)

Formosa Journal of Science and Technology (FJST)
Vol. 5 No. 8 (2026): August 2026

Post-harvest losses due to degradable quality of agricultural products during storage are a challenge in improving the efficiency of the food supply chain. This research aims to develop a Predictive Digital Twin model to predict changes in product quality based on storage conditions and quality characteristics. The study used an experimental quantitative approach with 50 samples of agricultural products through 13 observation periods, resulting in 650 observation points and 3,250 observation data from temperature, humidity, weight, color, and texture parameters. The data was analyzed using predictive modeling based on quality change trends and evaluated with Mean Absolute Error (MAE). The results showed the model was able to predict changes in quality with a low error rate. The research contributes to the development of data-driven smart agriculture to reduce post-harvest losses