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Ensuring food security in precision agriculture requires early prediction of soybean yield at various scales within the United States (U.S.), ranging from international to local levels. Accurate yield estimation is essential in preventing famine by providing insights into food availability during the growth season. Numerous deep learning (DL) algorithms have been developed to estimate soybean yield effectively using time-series remote sensing (RS) data to achieve this goal. However, the training data with short time spans can limit their ability to adapt to the dynamic and nuanced temporal changes in crop conditions. To address this challenge, we designed a 3D-ResNet-BiLSTM model to efficiently predict soybean yield at the county level across the U.S., even when using training data with shorter periods. We leveraged detailed Sentinel-2 imagery and Sentinel-1 SAR images to extract spectral bands, key vegetation indices (VIs), and VV and VH polarizations. Additionally, Daymet data was incorporated via Google Earth Engine (GEE) to enhance the model's input features. To process these inputs effectively, a dedicated 3D-ResNet architecture was designed to extract high-level features. These enriched features were then fed into a BiLSTM layer, enabling accurate prediction of soybean yield. To evaluate the efficacy of our model, its performance was compared with that of well-known models, including the Linear Regression (LR), Random Forest (RF), and 1D/2D/3D-ResNet models, as well as a 2D-CNN-LSTM model. The data from a short period (2019 to 2020) were used to train all models, while their accuracy was assessed using data from the year 2021. The experimental results showed that the proposed 3D-Resnet-BiLSTM model had a superior performance compared to the other models, achieving remarkable metrics (R2 = 0.791, RMSE = 5.56 Bu Ac−1, MAE = 4.35 Bu Ac−1, MAPE = 9%, and RRMSE = 10.49%). Furthermore, the 3D-ResNet-BiLSTM model showed a 7% higher R2 than the ResNet and RF models and an enhancement of 27% and 17% against the LR and 2D-CNN-LSTM models, respectively. The results highlighted our model's potential for accurate soybean yield predictions, supporting sustainable agriculture and food security. [ABSTRACT FROM AUTHOR]

Accurate stock price prediction has an important role in stock investment. Because stock price data are characterized by high frequency, nonlinearity, and long memory, predicting stock prices precisely is challenging. Various forecasting methods have been proposed, from classical time series methods to machine-learning-based methods, such as random forest (RF), recurrent neural network (RNN), convolutional neural network (CNN), Long Short-Term Memory (LSTM) neural networks and their variants, etc. Each method can reach a certain level of accuracy but also has its limitations. In this paper, a CNN-BiLSTM-Attention-based model is proposed to boost the accuracy of predicting stock prices and indices. First, the temporal features of sequence data are extracted using a convolutional neural network (CNN) and bi-directional long and short-term memory (BiLSTM) network. Then, an attention mechanism is introduced to fit weight assignments to the information features automatically; and finally, the final prediction results are output through the dense layer. The proposed method was first used to predict the price of the Chinese stock index—the CSI300 index and was found to be more accurate than any of the other three methods—LSTM, CNN-LSTM, CNN-LSTM-Attention. In order to investigate whether the proposed model is robustly effective in predicting stock indices, three other stock indices in China and eight international stock indices were selected to test, and the robust effectiveness of the CNN-BiLSTM-Attention model in predicting stock prices was confirmed. Comparing this method with the LSTM, CNN-LSTM, and CNN-LSTM-Attention models, it is found that the accuracy of stock price prediction is highest using the CNN-BiLSTM-Attention model in almost all cases. [ABSTRACT FROM AUTHOR]

As agriculture has an increasing impact on the environment, new techniques can help meet future food needs while maintaining or reducing the environmental footprint. Those techniques must incorporate a range of sensing, communication, and data analysis technologies to make informed management decisions, such as those related to the use of water, fertilizer, pesticides, seeds, fuel, labor, etc., to help increase crop production and reduce water and nutrient losses, as well as negative environmental impacts. In this study, a Bidirectional Long Short-Term Memory (BiLSTM) model was trained on real data from Internet of Things sensors in a vineyard located in the Douro wine-growing region, from 2018–2021, to evaluate the ability of this model to predict the Fraction of Transpirable Soil Water (FTSW). The model uses historical data, including reference evapotranspiration, relative humidity, vapor pressure deficit, and rainfall, and outputs the FTSW for periods of one, three, five, and seven days. The model achieved an RMSE between 8.3% and 16.6% and an R 2 -score between 0.75 and 0.93. The model was validated on an independent dataset collected in 2002–2004 from a different vineyard located in the Lisbon wine-growing region, Portugal, and achieved an R 2 -score of 87% and an RMSE of 10.36%. Finally, the performance of the FTSW in the vineyard prediction model was compared with that of the Random Forest model, support vector regression, and linear regression. The results showed that BiLSTM performed better than the RF model on the unseen data, and the BiLSTM model can be considered a suitable model for the accurate prediction of the FTSW. [ABSTRACT FROM AUTHOR]

Global Sea Surface Temperature (SST) trends have garnered significant attention in several ocean-related domains, including global warming, marine biodiversity, and environmental protection. This involves having an accurate and efficient forecast of future SST to ensure early detection and response in time to these events. Deep learning algorithms have become popular in SST prediction recently, although directly obtaining optimal prediction results from historical observation data is not simple. In this paper, we propose STA-SST, a new deep learning approach for forecasting SST, by combining the temporal dependencies of SST using the Bidirectional Long Short-Term Memory (BiLSTM) model, spatial features extracted from the convolution layer, and relevant information from the attention mechanism. To assess how well the Attention-BiLSTM with convolution layer predicts SST, we conducted a case study in the Moroccan Sea, concentrating on five different areas. The proposed model was compared against alternative forecasting models, including LSTM, XGBoost, Support Vector Regression (SVR), and Random Forest (RF). The experimental results show that STA-STT produces noticeably the best prediction results and is a solid choice for field implementation. • STA-SST combines BiLSTM, Conv1D, and attention mechanism for accurate SST forecasting. • Spatial features enhance the prediction of time series data. • STA-SST outperforms traditional models in SST forecasting. [ABSTRACT FROM AUTHOR]