Sign up for our daily Newsletter and stay up to date with all the latest news!

Subscribe I am already a subscriber

You are using software which is blocking our advertisements (adblocker).

As we provide the news for free, we are relying on revenues from our banners. So please disable your adblocker and reload the page to continue using this site.
Thanks!

Click here for a guide on disabling your adblocker.

Sign up for our daily Newsletter and stay up to date with all the latest news!

Subscribe I am already a subscriber

Researchers look to develop an intelligent control system to dry peony flowers

To enhance the drying quality of peony flowers, a recent study developed an integrated intelligent control and monitoring system.

The system incorporates computer vision technology to enable real‐time continuous monitoring and analysis of the total color change (ΔE) and shrinkage rate (SR) of the material. Additionally, by integrating drying time and temperature data, a hybrid neural network model combining convolutional neural networks, long short‐term memory, and attention mechanisms (CNN‐LSTM‐Attention) was employed to accurately predict the moisture ratio (MR) of peony flowers. The predictive model achieved a coefficient of determination (R²) of 0.9962, a mean absolute error (MAE) of 0.6870, and a root mean square error (RMSE) of 0.7634, demonstrating high accuracy in predicting moisture content during the drying process. Furthermore, the system utilized a fuzzy controller to dynamically regulate the drying parameters.

The fuzzy control strategy was used to shorten the drying time by approximately 1 h, improve the drying efficiency by roughly 12%, and significantly maintain the quality of peony flowers. These findings underscore the potential of the system to enhance drying efficiency and product quality.

Wang, Dong & Wang, Yong & Niu, Yao & Zhang, Weipeng & Li, Cunliang & Li, Pei & Zhang, Xuyang & Zhao, Yifan & Yuan, Yuejin. (2025). Development of an intelligent control system of high efficiency and online monitoring for hot air drying of peony flowers. Journal of Food Science. 90. 10.1111/1750-3841.17652.

Source: Research Gate

Publication date: