Numerical simulation and study on heat and mass transfer in a hybrid ultrasound/convective dryer.

Susceptibility of airborne ultrasonic power to augment heat and mass transfer during hot air dehydration of peppermint leaves was investigated in the present study. To predict the moisture removal curves, a unique non-equilibrium mathematical model was developed. For the samples dried at temperatures of 40‒70 °C and the power intensities of 0‒104 kW m-3, the diffusion of moisture inside the leaves and coefficients for of mass and heat transfer varied from 0.601 × 10-4 to 5.937 × 10-4 s-1, 4.693 × 10-4 to 7.975 × 10-4 m s-1 and 49.2 to 78.1 W m-2 K-1, respectively. In general, at the process temperatures up to 60 °C, all the studied transfer parameters were augmented in the presence of ultrasonic power.

Optimum drying conditions for ginger (Zingiber officinale Roscoe) based on time, energy consumption and physicochemical quality.

The effect of ultrasonic pre-treatment on moisture removal characteristics of ginger in a convective dryer was investigated. The slabs were dried by practicing sonication durations of 0, 15 and 30 min at different levels of the air temperature and velocity. Following increasing the sonication duration and air temperature, required time and energy to dehydrate the samples were decreased. The pre-treatment played important role in improving rehydration capability and surface color retention in the dried gingers. Content of the main volatile component (α-Zingiberene) was not influenced by the sonication. Mean values for the phenolic contents and antioxidant activity at sonication duration of 0, 15 and 30 min were determined to be 18.93, 18.15 and 17.49 GAE/g dry matter and 83.57, 78.33 and 74.58 %, respectively. The desired values for the temperature, velocity and sonication duration were revealed to be about 66 °C, 3 m/s and 20 min, respectively.

A Framework for Identification of Healthy Potted Seedlings in Automatic Transplanting System Using Computer Vision.

Automatic transplanting of seedlings is of great significance to vegetable cultivation factories. Accurate and efficient identification of healthy seedlings is the fundamental process of automatic transplanting. This study proposed a computer vision-based identification framework of healthy seedlings. Vegetable seedlings were planted in trays in the form of potted seedlings. Two-color index operators were proposed for image preprocessing of potted seedlings. An optimal thresholding method based on the genetic algorithm and the three-dimensional block-matching algorithm (BM3D) was developed to denoise and segment the image of potted seedlings. The leaf area of the potted seedling was measured by machine vision technology to detect the growing status and position information of the potted seedling. Therefore, a smart identification framework of healthy vegetable seedlings (SIHVS) was constructed to identify healthy potted seedlings. By comparing the identification accuracy of 273 potted seedlings images, the identification accuracy of the proposed method is 94.33%, which is higher than 89.37% obtained by the comparison method.