Visual analysis of sea buckthorn fruit moisture content based on deep image processing technology.

The effect of moisture content changes during drying processing on the appearance of sea buckthorn was studied. Using computer vision methods and various image processing methods to collect and analyze images during the drying process of sea buckthorn fruit. Sea buckthorn is dried in a drying oven at a temperature of 65 °C and Level 1 wind speed conditions. The images of the entire drying process of sea buckthorn fruit were collected at 30-min intervals. Deep mining and transformation of image information through various image processing methods. By calibrating and modeling the color components, real-time online detection of the moisture content of sea buckthorn fruit can be achieved. After modeling, this article attempted to use LSTM (Long Short Term Memory) to predict the appearance of sea buckthorn fruit with supercritical moisture content. Different agricultural products adapt to different color spaces, but after standard modeling with a certain amount of data, applying color components to detect moisture content is a very good method. • Seabuckthorn has a strong correlation between water content and appearance color. • The color component can detect the moisture content of seabuckthorn fruits. • Analyzed multiple color spaces to identify the optimal color components. • After modeling, use LSTM to predict the appearance of seabuckthorn fruits. • Adopting simple imaging sensors will have better application prospects. [ABSTRACT FROM AUTHOR]