Tianshu Wang

Chrysanthemum is a traditional Chinese medicinal herb that contains significant medicinal and economic value. However, the medicinal and economic value of chrysanthemum differs depending on its regions and types. Therefore, it is valuable to classify chrysanthemum accurately. Traditional classification methods are costly, time-consuming, and mainly rely on manual processes, chemical testing, or genetic analysis. In light of these challenges, this paper proposes a Chrysanthemum Classification via Color Space Fusion Transformer, which is both cost-effective and capable of real-time processing. First, the chrysanthemum images in the RGB color space are converted to the LAB color space. Second, a multi-path network is designed to independently extract color space features from both the RGB and LAB color spaces, followed by their integration through an inter-path fusion module. Finally, the Transformer module further analyzes the semantic characteristics of these extracted color space features. Experimental results indicate that the proposed method achieves superior accuracy and stability compared to existing classification methods, with a classification accuracy of 96.16%. This method provides an efficient and practical solution for chrysanthemum origin traceability.

Citri Reticulatae Pericarpium (CRP), the dried peel of citrus fruits, holds notable dietary and medicinal value. Its quality and price largely depend on origin and aging. Lower-grade CRP is often adulterated to imitate premium products, making accurate authentication of region and vintage essential for quality assurance and fair market valuation. Existing methods for vintage classification are limited due to complex equipment and high operational costs, restricting their scalability in practical applications. To address these issues, a convenient method for the accurate identification of Citri Reticulatae Pericarpium using image and multi-stream is proposed. The method comprises three main stages. Firstly, an object detection network with bounding box refinement localizes exocarp and albedo regions from whole CRP images. Secondly, a three-stream feature extractor processes the whole images along with exocarp and albedo patches to capture complementary visual details. A channel-level feature interaction module further enhances robustness through cross-region feature integration. Thirdly, a meta-learning module enables rapid adaptation to images captured under varying conditions by different consumer-grade devices. Experimental results demonstrate that the proposed method achieves an accuracy of 95.5% on iPhone-captured images. In addition, for images captured by different devices, the proposed method achieves a relative accuracy improvement of more than 34% over the direct transfer method, mainly owing to the meta-learning adaptation to different devices.

Body Mass Index (BMI) is a crucial indicator for assessing human obesity and overall health, providing valuable insights for applications such as health monitoring, patient re-identification, and personalized healthcare. Recently, several data-driven methods have been developed to estimate BMI using 2D and 3D features extracted from facial and body images or RGB-D data. However, current research faces challenges such as the incomplete consideration of anthropometric features, the neglect of multiplex networks, and low-BMI-estimation performance. To address these issues, this paper proposes three 3D anthropometric features, one 2D anthropometric feature, and a deep feature extraction method to comprehensively consider anthropometric features. Additionally, a BMI estimation method based on a multiplex network is introduced. In this method, three types of features are extracted by constructing a multichannel network, and BMI estimation is performed using Kernel Ridge Regression (KRR). The experimental results demonstrate that the proposed method significantly outperforms state-of-the-art methods. By incorporating symmetry into our analysis, we can uncover deeper patterns and relationships within complex systems, leading to a more comprehensive understanding of the phenomena under investigation.

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.

Chrysanthemum morifolium Ramat(hereinafter referred to as Chrysanthemum) is one of the most beloved and economically valuable Chinese herbal crops, which contains abundant medicinal ingredients and wide application prospects. Therefore, identifying the classification and origin of Chrysanthemum is important for producers, consumers, and market regulators. The existing Chrysanthemum classification methods mostly rely on visual subjective identification, are time-consuming, and always need high equipment costs. A novel method is proposed to accurately identify the Chrysanthemum classification in a swift, non-invasive, and non-contact way. The proposed method is based on the fusion of deep visual features of both the front and back sides. Firstly, the different Chrysanthemums images are collected and labeled with origins and classifications. Secondly, the background area with less available information is removed by image preprocessing. Thirdly, a two-stream feature extraction network is designed with two inputs which are the preprocessed front and back Chrysanthemum images. Meanwhile, the incorporation of single-path residual connections and cross-path residual connections is employed to extend the receptive field of the network and fully fusion the features from both the front and back sides. Experimental results demonstrate the proposed method outperforms existing methods, which gain higher accuracy and stability. The proposed method presents an effective and dependable solution for identifying the classification and origin of Chrysanthemum. Code and data are available at https://github.com/dart-into/CCMIFB.