Apples, as the fourth-largest globally produced fruit, play a crucial role in modern agriculture. However, accurately identifying apple diseases remains a significant challenge as failure in this regard leads to economic losses and poses threats to food safety. With the rapid development of artificial intelligence, advanced deep learning methods such as convolutional neural networks (CNNs) and Transformer-based technologies have made notable achievements in the agricultural field. In this study, we propose a dual-branch model named DBCoST, integrating CNN and Swin Transformer. CNNs focus on extracting local information, while Transformers are known for their ability to capture global information. The model aims to fully leverage the advantages of both in extracting local and global information. Additionally, we introduce the feature fusion module (FFM), which comprises a residual module and an enhanced Squeeze-and-Excitation (SE) attention mechanism, for more effective fusion and retention of both local and global information. In the natural environment, there are various sources of noise, such as the overlapping of apple branches and leaves, as well as the presence of fruits, which increase the complexity of accurately identifying diseases on apple leaves. This unique challenge provides a robust experimental foundation for validating the performance of our model. We comprehensively evaluate our model by conducting comparative experiments with other classification models under identical conditions. The experimental results demonstrate that our model outperforms other models across various metrics, including accuracy, recall, precision, and F1 score, achieving values of 97.32%, 97.33%, 97.40%, and 97.36%, respectively. Furthermore, detailed comparisons of our model’s accuracy across different diseases reveal accuracy rates exceeding 96% for each disease. In summary, our model performs better overall, achieving balanced accuracy across different apple leaf diseases.

Although Faster R-CNN has undergone a lot of improvements, it still exists a significant gap in the performance between the detection of small and large objects, mainly because the low-level network lacks semantic information and small objects are only involved in a few images. To mitigate the above issues, we propose an object detection model based on Multi-Scale Feature fusion Cross Stage Partial Network (MSF-CSPNet) in this paper. The proposed MSF-CSPNet focuses on the fusion of concrete features and abstract features from multi-scale feature by learning shallow features at the shallow level and deep features at the deep level. Meanwhile, the data augmentation is performed by using random horizontal flip. On the basis, the improved Faster-RCNN model with Automatic Mixed Precision, Group Batch Sampler and MSF-CSPNet was formed. The proposed algorithm is valuated on the Microsoft Common Objects in Context (MS COCO) 2017 and obtained leading performance with 5.4% improvement in APcoco, 5.9% improvement in AP50, 6.9% improvement in AP75, 5.8% improvement in APS, 6.1% improvement in APM, 5.8% improvement in APL compare to Faster R-CNN based on ResNet-50 with Feature Pyramid Network (FPN) backbone, and also outperformed previous reports on state-of-art Faster R-CNN series using other backbone networks, especially for small object detection. This research shows that the combination of a backbone with stronger learning ability and FPN is helpful to detect the expression of objects. Faster R-CNN based on MSF-CSPNet has high efficiency and better balance between accuracy and speed.

In addition to the conventional situation of detecting a single disease on a single leaf in corn leaves, there is a complex phenomenon of multiple diseases overlapping on a single leaf (compound diseases). Current research on corn leaf disease detection predominantly focuses on single leaves with single diseases, with limited attention given to the detection of compound diseases on a single leaf. However, the occurrence of compound diseases complicates the accuracy of traditional deep learning algorithms for disease detection, necessitating the exploration of new models for the identification of compound diseases on corn leaves. To achieve rapid and accurate identification of compound diseases in corn fields, this study adopts the YOLOv5s model as the base network, chosen for its smaller size and faster detection speed. We propose a corn leaf compound disease recognition method, YOLOv5s-C3CBAM, based on an attention mechanism. To address the challenge of limited data for corn leaf compound diseases, a CycleGAN model is employed to generate synthetic images. The scarcity of real data is thereby mitigated, facilitating the training of deep learning models with sufficient data. The YOLOv5s model is selected as the base network, and an attention mechanism is introduced to enhance the network’s focus on disease lesions while mitigating interference from compound diseases. This augmentation results in improved recognition accuracy. The YOLOv5s-C3CBAM compound disease recognition model, incorporating the attention mechanism, achieves an average precision of 83%, an F1 score of 81.98%, and a model size of 12.6 Mb. Compared to the baseline model, the average precision is improved by 3.1 percentage points. Furthermore, it outperforms Faster R-CNN and YOLOv7-tiny models by 27.57 and 2.7 percentage points, respectively. This recognition method demonstrates the ability to rapidly and accurately identify compound diseases on corn leaves, offering valuable insights for future research on precise identification of compound agricultural crop diseases in field conditions.

The dynamic changes of mobile terminals have led to the more complex environment for edge computing resource allocation. Edge nodes are generally mobile wireless devices or network devices with limited processing capacity, and have relatively limited computing resources. The importance of computing tasks for terminal devices changes dynamically , and the corresponding computing efficiency also changes. Therefore , the dynamic resource allocation is particularly important in the edge computing environments. In order to maximize the efficiency of using edge computing resources, increase the profit of edge node, and reduce the cost of edge terminal devices, exploring an efficient dynamic resource pricing and allocation mechanism for edge computing is an urgent problem that we need to solve. In this paper, 1 Springer Nature 2021 L A T E X template Dynamic Pricing in Edge computing Resource Allocation Based on Stackelberg Dynam we describe the dynamic pricing and resource allocation problem between edge node and terminal devices as a Stackelberg dynamic game model, and solve the Nash equilibrium solution of the model through Bellman dynamic programming theory. During the resource allocation process, to get the optimal computing resource price of edge node and the optimal computing resource usage of terminal devices.