Accurate sex identification in Procambarus clarkii is essential for genetic breeding and aquaculture management, as it helps optimize population structure, improve reproductive efficiency, and support sustainable aquaculture development. However, manual identification is time-consuming, labor-intensive, and prone to errors, especially when subtle visual differences need to be distinguished. To address this problem, this study proposed SCM-DETR, a sex identification method for Procambarus clarkii based on multi-dimensional feature fusion and enhancement. A high-resolution imaging system was used to acquire two-dimensional images of Procambarus clarkii, and a labeled dataset, the Procambarus clarkii gonad dataset (PGD), was constructed. To improve identification performance, a multi-dimensional semantics and details fusion method (MSDM) was designed to integrate high-level semantic information with fine-grained detail features, thereby enhancing feature representation and localization accuracy. In addition, a channel-spatial focus network (CSFN) was introduced to capture discriminative multidimensional features, including texture and color, for more accurate identification of subtle sex-related differences. Experimental results showed that SCM-DETR-R18 achieved 95.8% mAP@0.50 and 64.6% mAP@0.50-0.95 on the PGD, improving by 1.9 and 1.1 percentage points over the baseline model, respectively. The AP values of female and male gonads reached 93.3% and 96.5%, with gains of 3.3 and 1.9 percentage points, respectively. Moreover, the proposed model had the lowest parameter count (21.11 M) among all compared methods. The results of this study demonstrate that SCM-DETR can effectively improve automated sex identification in Procambarus clarkii and has good potential for intelligent aquaculture applications.

Oyster is the largest cultured shellfish in the world, and it has high economic value. The plumpness of the Pacific oyster gonad has important implications for the quality and breeding of subsequent parents. At present, only the conventional method of breaking their shells allows for the observation and study of the interior tissues of Pacific oysters. It is an important task to use computer technology for non-destructive sex detection of oysters and to select mature and full oysters for breeding. In this study, based on the multi-effect feature fusion network R-SINet algorithm, a CF-Net algorithm was designed through a boundary enhancement algorithm to detect inconspicuous objects that appear to be seamlessly embedded in the surrounding environment in nuclear magnetic resonance (NMR) images, effectively solving the problem of difficulty in distinguishing Pacific oyster gonads from background images. In addition, calculations were performed on the segmented gonadal regions to obtain a grayscale value difference map between male and female oysters. It was found that there were significant differences in grayscale values between females and males. This task allows for non-destructive detection of the gender of oysters. Firstly, a small animal magnetic resonance imaging (MRI) system was used to perform MRI on Pacific oysters, and a dataset of oyster gonads was established. Secondly, a gonadal segmentation model was created, and the Compact Pyramid Refinement Module and Switchable Excitation Model were applied to the R-SINet algorithm model to achieve multi-effect feature fusion. Then, the Convformer encoder, Token Reinforcement Module, and Adjacent Transfer Module were used together to form the CF-Net network algorithm, further improving the segmentation accuracy. The experimental results on the oyster gonad dataset have demonstrated the effectiveness of this method. Based on the segmentation results, it is possible to calculate the grayscale values of the gonadal region and obtain the distribution map of the grayscale value difference between male and female oysters. The results can provide a technical methodology for the non-destructive discrimination of oyster gender and later reproduction.

Livestock breeding is often required for animal growth and development. Among them, the systematic evaluation of livestock body measurements can also be highlighted to represent the animal growth and developmental stages. Such measurements are of great importance for the decision-making on the overall breeding. Manual contact measurements have been used in traditional practices. However, manual contact is usually susceptible to subjective errors, due to the cumbersome, time-consuming, and labor-intensive tasks. It is very necessary for the accurate data of the correct decisions. Fortunately, machine vision has revolutionized the agricultural industry in recent years. The contactless body measurement can be expected to replace the manual contact measurements using machine vision. The potential stress reactions can also be prevented to reduce the labor intensity in livestock breeding. This study aims to review the research progress of the non-contact livestock body measurement using machined vision. Four commonly large-bodied livestock were selected, including cattle, sheep, horses, and pigs. Initially, the common acquisition of livestock images was outlined to evaluate the types of imaging devices and various deployments. All tasks were aligned with the body size measurement. Subsequently, machine vision was applied to the contactless body measurements of livestock over the past five years. The current research status of image segmentation was also summarized during livestock body measurements. The speed, accuracy, and portability of equipment were then concentrated mainly on the body measurement at present. Several challenges were proposed, including the limited supply of public datasets and deep learning in the deployment of the algorithms in real-world environments. As such, the generative models can be expected to augment the dataset of the livestock images. Deep learning can be promoted to develop the generalized measurement suitable for a wide range of livestock. The findings can also provide valuable insights and references for future research.