The dissolved oxygen (DO) content is one of the important water quality parameters; it is crucial for assessing water body quality and ensuring the healthy growth of aquatic organisms. To enhance the prediction accuracy of DO in aquaculture, we propose a fused neural network model integrating a convolutional neural network (CNN) and a gated recurrent unit (GRU). This model initially employs a CNN to extract primary features from water quality parameters. Subsequently, the GRU captures temporal information and long-term dependencies, while a temporal attention mechanism (TAM) is introduced to further pinpoint crucial information. By optimizing model parameters through an improved particle swarm optimization (IPSO) algorithm, we develop a comprehensive IPSO-CNN-GRU-TAM prediction model. Experiments conducted using water quality datasets collected from Eagle Mountain Lake demonstrate that our model achieves a root mean square error (RMSE) of 0.0249 and a coefficient of determination (R²) of 0.9682, outperforming other prediction models with high precision. The model exhibits stable performance across fivefold cross-validation and datasets of varying depths, showcasing robust generalization capabilities. In summary, this model allows aquaculturists to precisely regulate the DO content, ensuring fish health and growth while achieving energy conservation and carbon reduction, aligning with the practical demands of modern aquaculture.

With the significant annual increase in market demand for biopesticides, the industrial production demand for predatory mites, which hold the largest market share among biopesticides, has also been rising. To achieve efficient and low-energy consumption control of predatory mite breeding environmental parameters, accurate estimation of breeding environmental parameters is necessary. This paper collects and pre-processes hourly time series data on temperature and humidity from industrial breeding environments. Time series prediction models such as SVR, LSTM, GRU, and LSTNet are applied to model and predict the historical data of the breeding environment. Experiments validate that the LSTNet model is more suitable for such environmental modeling. To further improve prediction accuracy, the training data for the LSTNet model is enhanced using hierarchical clustering of time series features. After augmentation, the root mean square error (RMSE) of the temperature prediction decreased by 27.3%, and the RMSE of the humidity prediction decreased by 32.8%, significantly improving the accuracy of the multistep predictions and providing substantial industrial application value.

In order to achieve the automated qualitative evaluation of Ephestia kuehniella eggs in mass industrialization and production, YOLOv10-CR is presented, with a modified circular representation based on YOLOv10. While existing methods like CircleNet struggle with overlapping objects, YOLOv10, though highly effective in general object detection, is not optimized for spherical objects. To address these limitations, we integrate YOLOv10’s efficient architecture with CircleNet’s circular representation, introducing a modified circle representation (xyy format) and an improved circle intersection over union (cIOU) algorithm. The proposed xyy circle representation reduces degrees of freedom by encoding circles through top and bottom boundary points, effectively disentangling overlapping eggs caused by adhesive mucus. The improved cIOU algorithm avoids singularity issues in nested or tangent circles, enhancing robustness under rotational variations. Experimental results demonstrate that the proposed YOLOv10-CR achieves better detection accuracy, computational efficiency, and rotation robustness with lower computational costs for the detection of Ephestia kuehniella eggs.