Short-term electrical load forecasting plays an important role in the safety, stability, and sustainability of the power production and scheduling process. An accurate prediction of power load can provide a reliable decision for power system management. To solve the limitation of the existing load forecasting methods in dealing with time-series data, causing the poor stability and non-ideal forecasting accuracy, this paper proposed an attention-based encoder-decoder network with Bayesian optimization to do the accurate short-term power load forecasting. Proposed model is based on an encoder-decoder architecture with a gated recurrent units (GRU) recurrent neural network with high robustness on time-series data modeling. The temporal attention layer focuses on the key features of input data that play a vital role in promoting the prediction accuracy for load forecasting. Finally, the Bayesian optimization method is used to confirm the model’s hyperparameters to achieve optimal predictions. The verification experiments of 24 h load forecasting with real power load data from American Electric Power (AEP) show that the proposed model outperforms other models in terms of prediction accuracy and algorithm stability, providing an effective approach for migrating time-serial power load prediction by deep-learning technology.

To realize tomato growth period monitoring and yield prediction of tomato cultivation, our study proposes a visual object tracking network called YOLO-deepsort to identify and count tomatoes in different growth periods. Based on the YOLOv5s model, our model uses shufflenetv2, combined with the CBAM attention mechanism, to compress the model size from the algorithm level. In the neck part of the network, the BiFPN multi-scale fusion structure is used to improve the prediction accuracy of the network. When the target detection network completes the bounding box prediction of the target, the Kalman filter algorithm is used to predict the target’s location in the next frame, which is called the tracker in this paper. Finally, calculate the bounding box error between the predicted bounding box and the bounding box output by the object detection network to update the parameters of the Kalman filter and repeat the above steps to achieve the target tracking of tomato fruits and flowers. After getting the tracking results, we use OpenCV to create a virtual count line to count the targets. Our algorithm achieved a competitive result based on the above methods: The mean average precision of flower, green tomato, and red tomato was 93.1%, 96.4%, and 97.9%. Moreover, we demonstrate the tracking ability of the model and the counting process by counting tomato flowers. Overall, the YOLO-deepsort model could fulfill the actual requirements of tomato yield forecast in the greenhouse scene, which provide theoretical support for crop growth status detection and yield forecast.

Smart agricultural greenhouses provide well-controlled conditions for crop cultivation but require accurate prediction of environmental factors to ensure ideal crop growth and management efficiency. Due to the limitations of existing predictors in dealing with massive, nonlinear, and dynamic temporal data, this study proposes a bidirectional self-attentive encoder–decoder framework (BEDA) to construct the long-time predictor for multiple environmental factors with high nonlinearity and noise in a smart greenhouse. Firstly, the original data are denoised by wavelet threshold filter and pretreatment operations. Secondly, the bidirectional long short-term-memory is selected as the fundamental unit to extract time-serial features. Then, the multi-head self-attention mechanism is incorporated into the encoder–decoder framework to improve the prediction performance. Experimental investigations are conducted in a practical greenhouse to accurately predict indoor environmental factors (temperature, humidity, and CO₂) from noisy IoT-based sensors. The best model for all datasets was the proposed BEDA method, with the root mean square error of three factors’ prediction reduced to 2.726, 3.621, and 49.817, and with an R of 0.749 for temperature, 0.848 for humidity, and 0.8711 for CO₂ concentration, respectively. The experimental results show that the favorable prediction accuracy, robustness, and generalization of the proposed method make it suitable to more precisely manage greenhouses.

In modern agriculture and environmental protection, effective identification of crop diseases and pests is very important for intelligent management systems and mobile computing application. However, the existing identification mainly relies on machine learning and deep learning networks to carry out coarse-grained classification of large-scale parameters and complex structure fitting, which lacks the ability in identifying fine-grained features and inherent correlation to mine pests. To solve existing problems, a fine-grained pest identification method based on a graph pyramid attention, convolutional neural network (GPA-Net) is proposed to promote agricultural production efficiency. Firstly, the CSP backbone network is constructed to obtain rich feature maps. Then, a cross-stage trilinear attention module is constructed to extract the abundant fine-grained features of discrimination portions of pest objects as much as possible. Moreover, a multilevel pyramid structure is designed to learn multiscale spatial features and graphic relations to enhance the ability to recognize pests and diseases. Finally, comparative experiments executed on the cassava leaf, AI Challenger, and IP102 pest datasets demonstrates that the proposed GPA-Net achieves better performance than existing models, with accuracy up to 99.0%, 97.0%, and 56.9%, respectively, which is more conducive to distinguish crop pests and diseases in applications for practical smart agriculture and environmental protection.

Diseases and pests are essential threat factors that affect agricultural production, food security supply, and ecological plant diversity. However, the accurate recognition of various diseases and pests is still challenging for existing advanced information and intelligence technologies. Disease and pest recognition is typically a fine-grained visual classification problem, which is easy to confuse the traditional coarse-grained methods due to the external similarity between different categories and the significant differences among each subsample of the same category. Toward this end, this paper proposes an effective graph-related high-order network with feature aggregation enhancement (GHA-Net) to handle the fine-grained image recognition of plant pests and diseases. In our approach, an improved CSP-stage backbone network is first formed to offer massive channel-shuffled features in multiple granularities. Secondly, relying on the multilevel attention mechanism, the feature aggregation enhancement module is designed to exploit distinguishable fine-grained features representing different discriminating parts. Meanwhile, the graphic convolution module is constructed to analyse the graph-correlated representation of part-specific interrelationships by regularizing semantic features into the high-order tensor space. With the collaborative learning of three modules, our approach can grasp the robust contextual details of diseases and pests for better fine-grained identification. Extensive experiments on several public fine-grained disease and pest datasets demonstrate that the proposed GHA-Net achieves better performances in accuracy and efficiency surpassing several other existing models and is more suitable for fine-grained identification applications in complex scenes.

In recent years, convolutional neural networks have made many advances in the field of computer vision. In smart greenhouses, using robots based on computer vision technology to pollinate flowers is one of the main methods of pollination. However, due to the complex lighting environment and the influence of leaf shadow in the greenhouse, it is difficult for the existing object detection algorithms to have high recall rate and accuracy. Based on this problem, from the perspective of application, we proposed a Yolov5s-based tomato flowering stage detection method named FlowerYolov5, which can well identify the bud phase, blooming phase and first fruit phase of tomato flowers. Firstly, in order to reduce the loss of tomato flower feature information in convolution and to strengthen the feature extraction of the target, FlowerYolov5 adds a new feature fusion layer. Then, in order to highlight the information of the object, the Convolutional Block Attention module (CBAM) is added to the backbone layer of FlowerYolov5. In the constructed tomato flower dataset, compared with YOLOv5s, the mAP of FlowerYolov5 increased by 7.8% (94.2%), and the F₁ score of FlowerYolov5 increased by 6.6% (89.9%). It was found that the overall parameter of FlowerYolov5 was 23.9 Mbyte, thus achieving a good balance between model parameter size and recognition accuracy. The experimental results show that the FlowerYolov5 has good robustness and more accurate precision. At the same time, the recall rate has also been greatly improved. The prediction results of the proposed algorithm can provide more accurate flower positioning for the pollination robot and improve its economic benefits.