Visual Detection of Water Content Range of Seabuckthorn Fruit Based on Transfer Deep Learning

To realize the classification of sea buckthorn fruits with different water content ranges, a convolution neural network (CNN) detection model of sea buckthorn fruit water content ranges was constructed. In total, 900 images of seabuckthorn fruits with different water contents were collected from 720 seabuckthorn fruits. Eight classic network models based on deep learning were used as feature extraction for transfer learning. A total of 180 images were randomly selected from the images of various water content ranges for testing. Finally, the identification accuracy of the network model for the water content range of seabuckthorn fruit was 98.69%, and the accuracy on the test set was 99.4%. The program in this study can quickly identify the moisture content range of seabuckthorn fruit by collecting images of the appearance and morphology changes during the drying process of seabuckthorn fruit. The model has a good detection effect for seabuckthorn fruits with different moisture content ranges with slight changes in characteristics. The migration deep learning can also be used to detect the moisture content range of other agricultural products, providing technical support for the rapid nondestructive testing of moisture contents of agricultural products.

Automated summary

To classify sea buckthorn fruits with different water content ranges, a convolutional neural network (CNN) detection model was constructed. 900 images of sea buckthorn fruits with different water contents were collected from 720 fruits, and eight classic deep learning network models were used for feature extraction and transfer learning. 180 images were randomly selected for testing, and the network model achieved an identification accuracy of 98.69% for the water content range of sea buckthorn fruit, with a test set accuracy of 99.4%. The program in this study can quickly identify the moisture content range of sea buckthorn fruit by collecting images during the drying process, and it can be applied to detect the moisture content range of other agricultural products.