In recent years, Pomacea canaliculata has aggressively invaded rice fields in Asia, resulting in significant agricultural losses. Biological control can effectively reduce the damage caused by P. canaliculata. This research evaluates E. sinensis as a biocontrol for P. canaliculata, focusing on its feeding preferences and optimal control density on snails of three sizes, as well as the effects on the nutritional quality of juvenile crabs post consumption. Our findings reveal that juvenile E. sinensis exhibit a strong preference for feeding on small snails, effectively managing populations at densities of 600 snails per tank. Crab feeding significantly reduces the survival and activity of snails. Furthermore, consumption of P. canaliculata meat alters the crabs’ physiology. Female crabs show elevated levels of molting hormones, total energy yield (TEY), and condition factor (CF), while males demonstrate increased lipid, moisture, and TEY levels. The amino acid profiles shift, with higher isoleucine and leucine levels in female hepatopancreas and reduced histidine in the muscles. Notably, the total Σn-3 PUFA content in female muscles fed on snail meat exceeds that of those given commodity grain. This study underscores the dual benefits of employing juvenile E. sinensis for managing P. canaliculata while enhancing crab farming practices.

Aquatic vegetation is critical for maintaining water quality, dissolved oxygen levels, and suitable habitats for crabs in aquaculture ponds. However, traditional manual surveys for monitoring aquatic plant growth and identifying invasive weeds are inefficient, labor-intensive, and prone to subjective errors. To address these limitations, this study proposes an integrated framework combining unmanned aerial vehicles (UAVs), remote sensing, and deep learning (DL) for pixel-level monitoring of aquatic plants in crab ponds. High-resolution UAV RGB and multispectral (MS) images were collected by a drone in Changxing County, Zhejiang Province, China. A specialized dataset was constructed, including cultivated Elodea canadensis and Hydrilla verticillata and two dominant invasive weed species (Alternanthera philoxeroides and Lemna minor). Next, an improved U2Net was designed to fuse the RGB-MS images and provide more detailed information. We also propose a SAM-based model, PondSAM, which integrates three modules: (1) a multiscale vision transformer encoder, (2) a self-generated prompt module, and (3) a multilayer aggregation decoder. The Pond-SAM results are relatively better than those of other state-of-the-art methods (e.g., SAM), achieving a Dice score of 0.865, an mIoU score of 0.858, and a recall score of 0.907. Additionally, the proposed method has been tested on the dataset with different light intensities and training ratios, and its reliability was verified. This study confirms that UAV remote sensing combined with deep learning provides a rapid, nondestructive, large-scale solution for aquatic plant monitoring and invasive weed identification in crab ponds, supporting precision aquaculture and ecological regulation.

Accurate identification of the hepatopancreas is essential for prawn quality assessment and automated seafood processing. This study presents an explainable deep learning framework for the binary classification of prawn images into “with hepatopancreas” and “no hepatopancreas” categories. A custom convolutional neural network (CNN) was developed using a dataset of 252 annotated images. To improve feature extraction from the limited dataset, an image preprocessing pipeline incorporating automatic contour-based cropping, contrast enhancement, and data augmentation was employed. The proposed model achieved a test accuracy of 97.37% and a calibrated full-dataset accuracy of 97.22%. Five-fold cross-validation yielded a mean accuracy of 96.83% ± 1.94%, indicating stable performance across different data partitions. Receiver operating characteristic analysis demonstrated satisfactory discriminative ability with AUC > 0.99. Gradient-weighted Class Activation Mapping (Grad-CAM) showed that the model primarily focused on biologically relevant hepatopancreas regions, improving the interpretability of the classification results. A graphical user interface was developed to enable rapid image analysis with visual feedback. Although further validation using larger and more diverse datasets is required, the proposed framework demonstrates the potential of explainable deep learning for automated prawn quality assessment and provides a practical foundation for intelligent seafood inspection applications.