Recently, the attention mechanism combining spatial and channel information has been widely used in various deep convolutional neural networks (CNNs), proving its great potential in improving model performance. However, this usually uses 2D global pooling operations to compress spatial information or scaling methods to reduce the computational overhead in channel attention. These methods will result in severe information loss. Therefore, we propose a Spatial channel attention mechanism that captures cross-dimensional interaction, which does not involve dimensionality reduction and brings significant performance improvement with negligible computational overhead. The proposed attention mechanism can be seamlessly integrated into any convolutional neural network since it is a lightweight general module. Our method achieves a performance improvement of 2.08% on ResNet and 1.02% on MobileNetV2 in top-one error rate on the ImageNet dataset.

Dissolved oxygen content is a key indicator of water quality in aquaculture environment. Because of its nonlinearity, dynamics, and complexity, which makes traditional methods face challenges in the accuracy and speed of dissolved oxygen content prediction. As a solution to these issues, this study introduces a hybrid model consisting of the Light Gradient Boosting Machine (LightGBM) and the Bidirectional Simple Recurrent Unit (BiSRU). Firstly, Linear interpolation and smoothing were used to identify significant parameters. LightGBM algorithm then determines the significance of dissolved oxygen by eliminating irrelevant variables and predicting dissolved oxygen in intensive aquaculture. Finally, the attention method was implemented to map the weighting and learning parameter matrices, so enabling the BiSRU’s hidden states to be assigned different weights. The findings shown that the presented prediction model can accurately anticipate the fluctuating trend of dissolved oxygen over a 10-day period in just 122 seconds, and the accuracy rate reached 96.28%. Comparing the model effects of LightGBM -BiSRU, LightGBM - GRU, LightGBM-LSTM, and BiSRU - Attention takes the least time. Its higher prediction accuracy can provide an essential reference for intensive aquaculture water quality regulation.

There are some problems with estrus detection in ewes in large-scale meat sheep farming: mainly, the manual detection method is labor-intensive and the contact sensor detection method causes stress reactions in ewes. To solve the abovementioned problems, we proposed a multi-objective detection layer neural network-based method for ewe estrus crawling behavior recognition. The approach we proposed has four main parts. Firstly, to address the problem of mismatch between our constructed ewe estrus dataset and the YOLO v3 anchor box size, we propose to obtain a new anchor box size by clustering the ewe estrus dataset using the K-means++ algorithm. Secondly, to address the problem of low model recognition precision caused by small imaging of distant ewes in the dataset, we added a 104 × 104 target detection layer, making the total target detection layer reach four layers, strengthening the model’s ability to learn shallow information and improving the model’s ability to detect small targets. Then, we added residual units to the residual structure of the model, so that the deep feature information of the model is not easily lost and further fused with the shallow feature information to speed up the training of the model. Finally, we maintain the aspect ratio of the images in the data-loading module of the model to reduce the distortion of the image information and increase the precision of the model. The experimental results show that our proposed model has 98.56% recognition precision, while recall was 98.04%, F1 value was 98%, mAP was 99.78%, FPS was 41 f/s, and model size was 276 M, which can meet the accurate and real-time recognition of ewe estrus behavior in large-scale meat sheep farming.

The optical-absorption and EPR spectra of octahedral ${\mathrm{Fe}}^{3+}$ center in yttrium aluminum garnet have been studied by diagonalizing the complete energy matrices for a $d^5$ configuration ion in a trigonal ligand field. It is shown that the local lattice structure around an octahedrally coordinated ${\mathrm{Fe}}^{3+}$ center has an expansion distortion. The expansion distortion may be attributed to the fact that the radius of ${\mathrm{Fe}}^{3+}$ ion is larger than that of ${\mathrm{Al}}^{3+}$ ion, and the ${\mathrm{Fe}}^{3+}$ ion will push the oxygen ligands outwards. Simultaneously, the local lattice structure distortion parameters $\Delta R=0.0907\mathrm{\AA }$ and $\Delta \theta =1.940°$ for the octahedral ${\mathrm{Fe}}^{3+}$ center in the crystal are determined. From optical spectra calculation, we confirm that two strong sharp transitions at 407 and $415\mathrm{nm}$, which are apparent in the optical absorption spectra for lightly doped $\mathrm{YAG}:{\mathrm{Fe}}^{3+}$ system, should be ascribed to the tetrahedral ${\mathrm{Fe}}^{3+}$ center. This is also in accord with the conclusion of Rotman et al. [J. Appl. Phys. 66, 3207 (1989)].

Accurate feeding control in aquaculture using fish feeding behavior state is key to improving feed utilization efficiency and reducing water pollution. A hybrid method based on ResNet34-CBAM and migration learning is proposed in this paper to achieve tilapia feeding behavior classification. In order to enhance the diversity of samples, pre-processing, such as scaling and cropping, and adding noise, was performed on the images. An underwater camera was used for taking pictures of tilapia behavior, followed by feature extraction using a pre-trained ResNet34 residual network, training with an improved classification for identifying feeding behavior. The testing findings demonstrate that the suggested technique in this article is 99.72% accurate in an actual environment. The model also outperforms other recognition models based on MobileNetV2, AlexNet, VGG11, ShuffleNet_v2_x0_5, and ResNet regarding accuracy, precision, and recall compared with other convolutional neural networks. Compared to the original model, the method shows a 7.84 percent improvement, indicating that it can correctly identify the feeding behavior of real fish in aquaculture and then scientifically determine the amount of baiting, which can improve feed utilization, increase aquaculture production, and decrease aquaculture costs.

In large-scale meat sheep farming, high CO₂ concentrations in sheep sheds can lead to stress and harm the healthy growth of meat sheep, so a timely and accurate understanding of the trend of CO₂ concentration and early regulation are essential to ensure the environmental safety of sheep sheds and the welfare of meat sheep. In order to accurately understand and regulate CO₂ concentrations in sheep barns, we propose a prediction method based on the RF-PSO-LSTM model. The approach we propose has four main parts. First, to address the problems of data packet loss, distortion, singular values, and differences in the magnitude of the ambient air quality data collected from sheep sheds, we performed data preprocessing using mean smoothing, linear interpolation, and data normalization. Second, to address the problems of many types of ambient air quality parameters in sheep barns and possible redundancy or overlapping information, we used a random forests algorithm (RF) to screen and rank the features affecting CO₂ mass concentration and selected the top four features (light intensity, air relative humidity, air temperature, and PM2.5 mass concentration) as the input of the model to eliminate redundant information among the variables. Then, to address the problem of manually debugging the hyperparameters of the long short-term memory model (LSTM), which is time consuming and labor intensive, as well as potentially subjective, we used a particle swarm optimization (PSO) algorithm to obtain the optimal combination of parameters, avoiding the disadvantages of selecting hyperparameters based on subjective experience. Finally, we trained the LSTM model using the optimized parameters obtained by the PSO algorithm to obtain the proposed model in this paper. The experimental results show that our proposed model has a root mean square error (RMSE) of 75.422 μg·m⁻³, a mean absolute error (MAE) of 51.839 μg·m⁻³, and a coefficient of determination (R²) of 0.992. The model prediction curve is close to the real curve and has a good prediction effect, which can be useful for the accurate prediction and regulation of CO₂ concentration in sheep barns in large-scale meat sheep farming.