An accurate prediction of cage-cultured water quality is a hot topic in smart mariculture. Since the mariculturing environment is always open to its surroundings, the changes in water quality parameters are normally nonlinear, dynamic, changeable, and complex. However, traditional forecasting methods have lots of problems, such as low accuracy, poor generalization, and high time complexity. In order to solve these shortcomings, a novel water quality prediction method based on the deep LSTM (long short-term memory) learning network is proposed to predict pH and water temperature. Firstly, linear interpolation, smoothing, and moving average filtering techniques are used to repair, correct, and de-noise water quality data, respectively. Secondly, Pearson’s correlation coefficient is used to obtain the correlation priors between pH, water temperature, and other water quality parameters. Finally, a water quality prediction model based on LSTM is constructed using the preprocessed data and its correlation information. Experimental results show that, in the short-term prediction, the prediction accuracy of pH and water temperature can reach 98.56% and 98.97%, and the time cost of the predictions is 0.273 s and 0.257 s, respectively. In the long-term prediction, the prediction accuracy of pH and water temperature can reach 95.76% and 96.88%, respectively.

Satellite remote sensing technology can achieve realtime observation of ships at sea, and the remote sensing images obtained have the advantages of high contrast and low noise and have become one of the important means of marine monitoring. For the satellite remote sensing image data, there are two main problems: (1) Remote sensing data class-imbalance problem; (2) the existing target detector in the presence of clouds, islands, farmed nets and other interferences on the small target ship there is a leakage of detection and wrong detection problem. To address the above problems, firstly, a new dataset containing 3881 images of remotely sensed ships in a variety of complex environments is constructed, which contains a total of 8418 ship instances. Secondly, we propose CSDP-YOLO for small target ship detection method with remote sensing data class imbalance. In order to enhance the performance of neural networks for small target ship detection in remote sensing images, the innovative CSDP module is proposed, which uses deep large kernel convolution to enhance the sensory field of shallow features and mixes the channel positions using point convolution to obtain a more excellent feature extraction performance. Finally, the MPDIoU loss function is introduced to solve the class imbalance problem between remote sensing small target ships and the background. We compare the performance with other state-of-the-art algorithms. The experimental results show that the proposed CSDP-YOLO algorithm can significantly improve the performance of small target ship detection for private datasets. Its average precision, recall, and AP50 are improved to 90.1%, 86.6%, and 91.4%, respectively. For the SSDD public remote sensing dataset, its metrics can reach the highest 93.6%, 93.7%, and 96.8%, respectively. For datasets and codes related to this article, please contact the corresponding author

In the smart mariculture, the timely and accurate predictions of water quality can help farmers take countermeasures before the ecological environment deteriorates seriously. However, the openness of the mariculture environment makes the variation of water quality nonlinear, dynamic and complex. Traditional methods face challenges in prediction accuracy and generalization performance. To address these problems, an accurate water quality prediction scheme is proposed for pH, water temperature and dissolved oxygen. First, we construct a new huge raw data set collected in time series consisting of 23,204 groups of data. Then, the water quality parameters are preprocessed for data cleaning successively through threshold processing, mean proximity method, wavelet filter, and improved smoothing method. Next, the correlation between the water quality to be predicted and other dynamics parameters is revealed by the Pearson correlation coefficient method. Meanwhile, the data for training is weighted by the discovered correlation coefficients. Finally, by adding a backward SRU node to the training sequence, which can be integrated into the future context information, the deep Bi-S-SRU (Bi-directional Stacked Simple Recurrent Unit) learning network is proposed. After training, the prediction model can be obtained. The experimental results demonstrate that our proposed prediction method achieve higher prediction accuracy than the method based on RNN (Recurrent Neural Network) or LSTM (Long Short-Term Memory) with similar or less time computing complexity. In our experiments, the proposed method takes 12.5ms to predict data on average, and the prediction accuracy can reach 94.42% in the next 3~8 days.

Infrared small target suffers from the lack of intrinsic features, context, and samples. Conventional detection methods are usually unable to sufficiently and effectively extract the features of infrared small targets. Therefore, we propose a novel attention-based local contrast learning network (ALCL-Net). Considering the scarcity of intrinsic features of infrared small targets, we propose ResNet32, which enhances the ability to extract infrared small target features and avoids the problem that the target features are overwhelmed by the background features due to too deep network. At the same time, we construct a simplified bilinear interpolation attention module (SBAM), which is used for fusion of hierarchical feature maps. It has fast inference speed and can focus on the feature of the target in the lack of context. Furthermore, local contrast learning (LCL) is introduced, which adopts the local contrast idea of nondeep learning methods. It can alleviate the dependence on dataset samples, thereby improving detection accuracy on datasets with few samples. Compared with the state-of-the-art methods, the proposed ALCL-Net achieves superior performance with an intersection over union (IoU) of 0.792 and a normalized IoU (nIoU) of 0.771 on the public single-frame infrared small target (SIRST) dataset.