Wheat disease detection is crucial for disease diagnosis, pesticide application optimization, disease control, and wheat yield and quality improvement. However, the detection of wheat diseases is difficult due to their various types. Detecting wheat diseases in complex fields is also challenging. Traditional models are difficult to apply to mobile devices because they have large parameters, and high computation and resource requirements. To address these issues, this paper combines the residual module and the inception module to construct a lightweight multiscale CNN model, which introduces the CBAM and ECA modules into the residual block, enhances the model’s attention to diseases, and reduces the influence of complex backgrounds on disease recognition. The proposed method has an accuracy rate of 98.7% on the test dataset, which is higher than classic convolutional neural networks such as AlexNet, VGG16, and InceptionresnetV2 and lightweight models such as MobileNetV3 and EfficientNetb0. The proposed model has superior performance and can be applied to mobile terminals to quickly identify wheat diseases.

Wheat production safety is facing serious challenges because wheat yellow rust is a worldwide disease. Wheat yellow rust may have no obvious external manifestations in the early stage, and it is difficult to detect whether it is infected, but in the middle and late stages of onset, the symptoms of the disease are obvious, though the severity is difficult to distinguish. A traditional deep learning network model has a large number of parameters, a large amount of calculation, a long time for model training, and high resource consumption, making it difficult to transplant to mobile and edge terminals. To address the above issues, this study proposes an optimized GhostNetV2 approach. First, to increase communication between groups, a channel rearrangement operation is performed on the output of the Ghost module. Then, the first five G-bneck layers of the source model GhostNetV2 are replaced with Fused-MBConv to accelerate model training. Finally, to further improve the model’s identification of diseases, the source attention mechanism SE is replaced by ECA. After experimental comparison, the improved algorithm shortens the training time by 37.49%, and the accuracy rate reaches 95.44%, which is 2.24% higher than the GhostNetV2 algorithm. The detection accuracy and speed have major improvements compared with other lightweight model algorithms.

The grade of wheat quality depends on the proportion of unsound kernels. Therefore, the rapid detection of unsound wheat kernels is important for wheat rating and evaluation. However, in practice, unsound kernels are hand-picked, which makes the process time-consuming and inefficient. Meanwhile, methods based on traditional image processing cannot divide adherent particles well. To solve the above problems, this paper proposed an unsound wheat kernel recognition algorithm based on an improved mask RCNN. First, we changed the feature pyramid network (FPN) to a bottom-up pyramid network to strengthen the low-level information. Then, an attention mechanism (AM) module was added between the feature extraction network and the pyramid network to improve the detection accuracy for small targets. Finally, the regional proposal network (RPN) was optimized to improve the prediction performance. Experiments showed that the improved mask RCNN algorithm could identify the unsound kernels more quickly and accurately while handling adhesion problems well. The precision and recall were 86% and 91%, respectively, and the inference time on the test set with about 200 targets for each image was 7.83 s. Additionally, we compared the improved model with other existing segmentation models, and experiments showed that our model achieved higher accuracy and performance than the other models, laying the foundation for wheat grading.

Power line extraction is not only crucial for UAVs obstacle avoidance, but also a fundamental step for fault diagnosis of power lines. Therefore, achieving robust and accurate extraction of power lines in aerial images is essential to enable intelligent UAVs inspection. Unfortunately, power line extraction is an extremely challenging task, and all the current methods attempt to utilize a single model to solve the problem of power line extraction in complex and variable scenes. This results in insufficient generalization ability and suboptimal computational efficiency. In this work, we propose a power line scene classification network based on complexity assessment, named SceneNet, which can provide a solution for tackling power line extraction challenges. Firstly, we propose a human-machine hybrid reasoning model to obtain the ground truth of image complexity reasonably and build the first benchmark dataset that can be used for automatic classification research of power line scenes. Secondly, we propose an improved StyleGAN3 model and loop transfer learning strategy for data augmentation. Most importantly, the SceneNet comprises a multi-feature joint embedding module and a feature encoding-decoding module. On the one hand, it achieves the multi-level fusion of artificial features and high-dimensional semantic features. On the other hand, we use a self-attention mechanism to enable full use of the contextual association between each block of the fusion feature map. The SceneNet has successfully achieved the mapping and pattern recognition between the abstract concept and the concrete features. Experimental results demonstrate that the SceneNet is obviously superior to the existing 12 state-of-the-art models, and it provides guidance and delineation of applicable scenes for power line extraction methods.

Cloud computing has gained widespread recognition for facilitating myriad online services and applications. However, the current stages of commercial cloud computing employ a moderate design, wherein computational resources like storage and servers are housed in a few sizable worldwide data centers. System reliability, efficiency, and low latency are all goals of virtual machine (VM) placement. Load balancing has emerged as a crucial challenge for attaining energy efficiency in a fictitious grid computing architecture where a variety of users’ workloads are distributed across several virtual machines. We propose a more effective optimization technique known as the twin fold moth flame algorithm. This algorithm considers multiple constraints, including computation time, stability, and placement cost. The proposed model’s effectiveness will be evaluated based on relocation costs, reaction times, and stability assessments. The most significant gains of the presented work are 4.24%, 9.73%, 11.10%, 28.83%, 7.63%, and 10.62% for 20 count data of nodes for artificial bee colony–bat algorithm, ant colony optimization, crow search algorithm, krill herd, whale optimization genetic algorithm, and improved Lévy-based whale optimization algorithm, respectively.

Robust power line detection from aerial images is an essential step for intelligent unmanned aerial vehicles (UAVs) inspection. However, compared to regular object detection tasks, the generalization ability and accuracy of current methods are seriously insufficient due to the variability of power line scenes and the low proportion of power lines in the image. In this letter, we propose an object-based Markov random field with discrete multi-neighborhood system (OMRF-DMNS) model, which can provide a solution for tackling power line detection challenges. Firstly, the proposed method constructs a discrete multi-neighborhood system (DMNS) for the non-contact nodes. This not only greatly improves the anti-noise ability of the model, but also lays the foundation for the transfer of context information among nodes under different receptive fields. Secondly, a multi-level logical model with multi-neighborhood joint reasoning (MLL-MNJR) is defined on the DMNS, which makes full use of the line features and location information to achieve semantic inference of nodes. Therefore, the OMRF-DMNS model can fully mine the multi-dimensional feature associations and context information among nodes, enabling it to effectively distinguish between power line segments and interference noises. Experimental results demonstrate that the proposed method significantly outperforms previous methods in different power line scenes.