The complexity and challenging underwater environment leading to degradation in underwater image. Measuring the quality of underwater image is a significant step for the subsequent image processing step. Existing Image Quality Assessment (IQA) methods do not fully consider the characteristics of degradation in underwater images, which limits their performance in underwater image assessment. To address this problem, an Underwater IQA (UIQA) method based on color space multi-feature fusion is proposed to focus on underwater image. The proposed method converts underwater images from RGB color space to CIELab color space, which has a higher correlation to human subjective perception of underwater visual quality. The proposed method extract histogram features, morphological features, and moment statistics from luminance and color components and concatenate the features to obtain fusion features to better quantify the degradation in underwater image quality. After features extraction, support vector regression(SVR) is employed to learn the relationship between fusion features and image quality scores, and gain the quality prediction model. Experimental results on the SAUD dataset and UIED dataset show that our proposed method can perform well in underwater image quality assessment. The performance comparisons on LIVE dataset, TID2013 dataset,LIVEMD dataset,LIVEC dataset and SIQAD dataset demonstrate the applicability of the proposed method.

Geographic origins play a vital role in traditional Chinese medicinal materials. Using the geo-authentic crude drug can improve the curative effect. The main producing areas of Chinese wolfberry are Ningxia, Gansu, Qinghai, and so on. The geographic origin of Chinese wolfberry can affect its texture, shape, color, smell, nutrients, etc. However, the traditional method for identifying the geographic origin of Chinese wolfberries is still based on human eyes. To efficiently identify Chinese wolfberries from different origins, this paper presents an intelligent identification method for Chinese wolfberries based on color space transformation and texture morphological features. The first step is to prepare the Chinese wolfberry samples and collect the image data. Then the images are preprocessed, and the texture and morphology features of single wolfberry images are extracted. Finally, the random forest algorithm is employed to establish a model of the geographic origin of Chinese wolfberries. The proposed method can accurately predict the origin information of a single wolfberry image and has the advantages of low cost, fast recognition speed, high recognition accuracy, and no damage to the sample.

Since ginger has characteristics of both food and medicine, it has a significant market demand worldwide. To effectively store ginger and achieve the drying and color enhancement effects required for better sales, it is often subjected to sulfur fumigation. Although sulfur fumigation methods can effectively prevent ginger from becoming moldy, they cause residual sulfur dioxide, harming human health. Traditional sulfur detection methods face disadvantages such as complex operation, high time consumption, and easy consumption. This paper presents a sulfur-fumigated ginger detection method based on natural image recognition. By directly using images from mobile phones, the proposed method achieves non-destructive testing and effectively reduces operational complexity. First, four mobile phones of different brands are used to collect images of sulfur- and non-sulfur-fumigated ginger samples. Then, the images are preprocessed to remove the blank background in the image and a deep neural network is designed to extract features from ginger images. Next, the recognition model is generated based on the features. Finally, meta-learning parameters are introduced to enable the model to learn and adapt to new tasks, thereby improving the adaptability of the model. Thus, the proposed method can adapt to different devices in its real application. The experimental results indicate that the recall rate, F1 score, and AUC-ROC of the four different mobile phones are more than 0.9, and the discrimination accuracy of these phones is above 0.95. Therefore, this method has good predictive ability and excellent practical value for identifying sulfur-fumigated ginger.