Abstract
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.
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