The aquatic environment in aquaculture serves as the foundation for the survival and growth of aquatic animals, while a high-quality water environment is a necessary condition for promoting efficient and healthy aquaculture development. To effectively guide early warnings and the regulation of water quality in aquaculture, this study proposes a predictive model based on a dual-channel and dual-attention mechanism, namely, the DAM-ResNet-LSTM model. This model encompasses two parallel feature extraction channels: a residual network (ResNet) and long short-term memory (LSTM), with dual-attention mechanisms integrated into each channel to enhance the model’s feature representation capabilities. Then, the proposed model is trained, validated, and tested using water quality and meteorological parameter data collected by an offshore farm environmental monitoring system. The results demonstrate that the proposed dual-channel structure and dual-attention mechanism can significantly improve the predictive performance of the model. The prediction accuracy for pH, dissolved oxygen (DO), and salinity (SAL) (with Nash coefficients of 0.9361, 0.9396, and 0.9342, respectively) is higher than that for chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), nitrite (NO₂⁻), and active phosphate (AP) (with Nash coefficients of 0.8578, 0.8542, 0.8372, and 0.8294, respectively). Compared to the single-channel model DA-ResNet (ResNet integrated with the proposed dual-attention mechanism), the Nash coefficients for predicting pH, DO, SAL, COD, NH₃-N, NO₂⁻, and AP increase by 12.76%, 12.58%, 11.68%, 18.350%, 19.32%, 16%, and 14.99%, respectively. Compared to the single-channel DA-LSTM model (LSTM integrated with the proposed dual-attention mechanism), the corresponding increases in Nash coefficients are 9.15%, 9.93%, 9.11%, 10.91%, 10.11%, 10.39%, and 10.2%, respectively. Compared to the ResNet-LSTM (ResNet and LSTM in parallel) model without the attention mechanism, the improvements in Nash coefficients are 1.91%, 2.4%, 0.74%, 3.41%, 2.71%, 3.55%, and 4.13%, respectively. The predictive performance of the model fulfills the practical requirements for accurate forecasting of water quality in nearshore aquaculture.

Underwater images frequently exhibit color distortion, detail blurring, and contrast degradation due to absorption and scattering by the underwater medium. This study proposes a progressive color correction strategy integrated with a vision-inspired image enhancement framework to address these issues. Specifically, the progressive color correction process includes adaptive color quantization-based global color correction, followed by guided filter-based local color refinement, aiming to restore accurate colors while enhancing visual perception. Within the vision-inspired enhancement framework, the color-adjusted image is first decomposed into a base layer and a detail layer, corresponding to low- and high-frequency visual information, respectively. Subsequently, detail enhancement and noise suppression are applied in the detail pathway, while global brightness correction is performed in the structural pathway. Finally, results from both pathways are fused to yield the enhanced underwater image. Extensive experiments on four datasets verify that the proposed method effectively handles the aforementioned underwater enhancement challenges and significantly outperforms state-of-the-art techniques.

The printed circuit board (PCB), a core component of electronic products, is playing an increasingly critical role in quality defect detection. Traditional methods suffer from low efficiency and high missed detection rates, rendering them insufficient to meet the industrial requirements for PCB defect detection. To address this issue, this paper proposes an ESI-YOLOv11n model for PCB defect detection that incorporates multi-scale feature fusion. The specific improvements are as follows: First, Spatial and Channel Reconstruction Convolution (ScConv) is incorporated to optimize the C3k2 module, creating a dynamic adaptive feature extraction unit that strengthens its ability to capture features of small defects. Second, an Efficient Multi-Scale Attention (EMA) mechanism is integrated into the Neck layer, dynamically adjusting the weight distribution of multi-scale feature maps to enhance efficiency of feature fusion and improve detection performance. Finally, the Inner concept is integrated with the CIoU loss function, resulting in the novel Inner-CIoU loss function. This loss function optimizes the model by utilizing auxiliary box mechanisms and geometric constraints, leading to more accurate regression results. Experimental results show that the improved model achieves an average precision of 95.9% and a recall rate of 93.3%, which are 9.3% and 11.5% higher than those of the original model, respectively, while having a parameter size of only 13.3 Mb. The model effectively reduces the missed detection rate and false detection rate, significantly enhances the PCB defect detection performance, and demonstrates superior comprehensive performance compared with current mainstream detection models.

Accurate water quality prediction is crucial for the sustainable management of mariculture, yet it faces challenges in modeling high-dimensional nonlinear data and optimizing model hyperparameters efficiently. To address this, a novel deep learning model named ICPO-ACNN-BiGRU-CA is proposed, which integrates an Improved Crested Porcupine Optimizer (ICPO) with a cross-attention (CA) mechanism. The model utilizes an Attention-based Convolutional Neural Network (ACNN) and a Bidirectional Gated Recurrent Unit (BiGRU) to extract spatio-temporal features, while the CA mechanism dynamically fuses these features. The ICPO algorithm is applied to optimize the model’s hyperparameters automatically. Validation on a dataset from a Zhanjiang aquaculture zone shows that the model achieves high prediction accuracy for key parameters, including water temperature (R² = 0.939), dissolved oxygen (R² = 0.935), pH (R² = 0.927), and electrical conductivity (R² = 0.930), while reducing the mean absolute error by 41.4–62.8%. These results demonstrate that the proposed model significantly enhances prediction accuracy and robustness, providing a reliable tool for real-time water quality warning and precision management in mariculture systems.