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.

The green-making stage is crucial for forming the distinctive aroma and flavor of Tieguanyin tea. Current green-making stage recognition relies on tea makers’ sensory experience, which is labor-intensive and time-consuming. To address these issues, this paper proposes a lightweight automatic recognition model named T-GSR for the accurate and objective identification of Tieguanyin tea green-making stages. First, an extensive set of Tieguanyin tea images at different green-making stages was collected. Subsequently, preprocessing techniques, i.e., multi-color-space fusion and morphological filtering, were applied to enhance the representation of target tea features. Furthermore, three targeted improvements were implemented based on the MobileNet V3 backbone network: (1) an adaptive residual branch was introduced to strengthen feature propagation; (2) the Rectified Linear Unit (ReLU) activation function was replaced with the Gaussian Error Linear Unit (GELU) to improve gradient propagation efficiency; and (3) an Improved Coordinate Attention (ICA) mechanism was adopted to replace the original Squeeze-and-Excitation (SE) module, enabling more accurate capture of complex tea features. Experimental results demonstrate that the T-GSR model outperforms the original MobileNet V3 in both classification performance and model complexity, achieving a recognition accuracy of 93.38%, an F1-score of 93.33%, with only 3.025 M parameters and 0.242 G FLOPs. The proposed model offers an effective solution for the intelligent recognition of Tieguanyin tea green-making stages, facilitating online monitoring and supporting automated tea production.