Agricultural Robot for Intelligent Detection of Pyralidae Insects

The Pyralidae insects are one of the main pests in economic crops. However, the manual detection and identification of Pyralidae insects are labor intensive and inefficient, and subjective factors can influence recognition accuracy. To address these shortcomings, an insect monitoring robot and a new method to recognize the Pyralidae insects are presented in this chapter. Firstly, the robot gets images by performing a fixed action and detects whether there are Pyralidae insects in the images. The recognition method obtains the total probability image by using reverse mapping of histogram and multi-template images, and then image contour can be extracted quickly and accurately by using constraint Otsu. Finally, according to the Hu moment characters, perimeter, and area characters, the contours can be filtrated, and recognition results with triangle mark can be obtained. According to the recognition results, the speed of the robot car and mechanical arm can be adjusted adaptively. The theoretical analysis and experimental results show that the proposed scheme has high timeliness and high recognition accuracy in the natural planting scene

Group channel pruning and spatial attention distilling for object detection

Due to the over-parameterization of neural networks, many model compression methods based on pruning and quantization have emerged. They are remarkable in reducing the size, parameter number, and computational complexity of the model. However, most of the models compressed by such methods need the support of special hardware and software, which increases the deployment cost. Moreover, these methods are mainly used in classification tasks, and rarely directly used in detection tasks. To address these issues, for the object detection network we introduce a three-stage model compression method: dynamic sparse training, group channel pruning, and spatial attention distilling. Firstly, to select out the unimportant channels in the network and maintain a good balance between sparsity and accuracy, we put forward a dynamic sparse training method, which introduces a variable sparse rate, and the sparse rate will change with the training process of the network. Secondly, to reduce the effect of pruning on network accuracy, we propose a novel pruning method called group channel pruning. In particular, we divide the network into multiple groups according to the scales of the feature layer and the similarity of module structure in the network, and then we use different pruning thresholds to prune the channels in each group. Finally, to recover the accuracy of the pruned network, we use an improved knowledge distillation method for the pruned network. Especially, we extract spatial attention information from the feature maps of specific scales in each group as knowledge for distillation. In the experiments, we use YOLOv4 as the object detection network and PASCAL VOC as the training dataset. Our method reduces the parameters of the model by 64.7 % and the calculation by 34.9%.Comment: Appl Intel

A YOLOv7 incorporating the Adan optimizer based corn pests identification method

Major pests of corn insects include corn borer, armyworm, bollworm, aphid, and corn leaf mites. Timely and accurate detection of these pests is crucial for effective pests control and scientific decision making. However, existing methods for identification based on traditional machine learning and neural networks are limited by high model training costs and low recognition accuracy. To address these problems, we proposed a YOLOv7 maize pests identification method incorporating the Adan optimizer. First, we selected three major corn pests, corn borer, armyworm and bollworm as research objects. Then, we collected and constructed a corn pests dataset by using data augmentation to address the problem of scarce corn pests data. Second, we chose the YOLOv7 network as the detection model, and we proposed to replace the original optimizer of YOLOv7 with the Adan optimizer for its high computational cost. The Adan optimizer can efficiently sense the surrounding gradient information in advance, allowing the model to escape sharp local minima. Thus, the robustness and accuracy of the model can be improved while significantly reducing the computing power. Finally, we did ablation experiments and compared the experiments with traditional methods and other common object detection networks. Theoretical analysis and experimental result show that the model incorporating with Adan optimizer only requires 1/2-2/3 of the computing power of the original network to obtain performance beyond that of the original network. The mAP@[.5:.95] (mean Average Precision) of the improved network reaches 96.69% and the precision reaches 99.95%. Meanwhile, the mAP@[.5:.95] was improved by 2.79%-11.83% compared to the original YOLOv7 and 41.98%-60.61% compared to other common object detection models. In complex natural scenes, our proposed method is not only time-efficient and has higher recognition accuracy, reaching the level of SOTA

The Anonymization Protection Algorithm Based on Fuzzy Clustering for the Ego of Data in the Internet of Things

In order to enhance the enthusiasm of the data provider in the process of data interaction and improve the adequacy of data interaction, we put forward the concept of the ego of data and then analyzed the characteristics of the ego of data in the Internet of Things (IOT) in this paper. We implement two steps of data clustering for the Internet of things; the first step is the spatial location of adjacent fuzzy clustering, and the second step is the sampling time fuzzy clustering. Equivalent classes can be obtained through the two steps. In this way we can make the data with layout characteristics to be classified into different equivalent classes, so that the specific location information of the data can be obscured, the layout characteristics of tags are eliminated, and ultimately anonymization protection would be achieved. The experimental results show that the proposed algorithm can greatly improve the efficiency of protection of the data in the interaction with others in the incompletely open manner, without reducing the quality of anonymization and enhancing the information loss. The anonymization data set generated by this method has better data availability, and this algorithm can effectively improve the security of data exchange

Multiorder Fusion Data Privacy-Preserving Scheme for Wireless Sensor Networks

Privacy-preserving in wireless sensor networks is one of the key problems to be solved in practical applications. It is of great significance to solve the problem of data privacy protection for large-scale applications of wireless sensor networks. The characteristics of wireless sensor networks make data privacy protection technology face serious challenges. At present, the technology of data privacy protection in wireless sensor networks has become a hot research topic, mainly for data aggregation, data query, and access control of data privacy protection. In this paper, multiorder fusion data privacy-preserving scheme (MOFDAP) is proposed. Random interference code, random decomposition of function library, and cryptographic vector are introduced for our proposed scheme. In multiple stages and multiple aspects, the difficulty of cracking and crack costs are increased. The simulation results demonstrate that, compared with the typical Slice-Mix-AggRegaTe (SMART) algorithm, the algorithm proposed in this paper has a better data privacy-preserving ability when the traffic load is not very heavy

Weight-Aware Sensor Deployment in Wireless Sensor Networks for Smart Cities

During the construction of wireless sensor networks (WSNs) for smart cities, a preliminary survey of the relative criticalness within the monitored area can be performed. It is a challenge for deterministic sensor deployment to balance the tradeoff of sensing reliability and cost. In this paper, based on the sensing accuracy of the sensor, we establish a reliability model of the sensing area which is divided into sensing grids, and different weights are allocated to those grids. We employ a practical evaluation criterion using seesaw mapping for determining the weights of sensing grids. We further formulate and solve an optimization problem for maximizing the trust degree of the WSNs. With our proposed method, the efficient deployment of sensors can be realized. Simulation results show that our proposed deployment strategy can achieve higher trust degree with reduced sensor deployment cost and lower number of sensors at a certain miss probability threshold

Remote Sensing Image Segmentation of Mariculture Cage Using Ensemble Learning Strategy

In harbour areas, the irrational layout and high density of mariculture cages can lead to a dramatic deterioration of the culture’s ecology. Therefore, it is important to analyze and regulate the distribution of cages using intelligent analysis based on deep learning. We propose a remote sensing image segmentation method based on the Swin Transformer and ensemble learning strategy. Firstly, we collect multiple remote sensing images of cages and annotate them, while using data expansion techniques to construct a remote sensing image dataset of mariculture cages. Secondly, the Swin Transformer is used as the backbone network to extract the remote sensing image features of cages. A strategy of alternating the local attention module and the global attention module is used for model training, which has the benefit of reducing the attention computation while exchanging global information. Then, the ensemble learning strategy is used to improve the accuracy of remote sensing cage segmentation. We carry out quantitative and qualitative analyses of remote sensing image segmentation of cages at the ports of Li’an, Xincun and Potou in Hainan Province, China. The results show that our proposed segmentation scheme has significant performance improvement compared to other models. In particular, the mIoU reaches 82.34% and pixel accuracy reaches 99.71%

A Robust Data Interpolation Based on a Back Propagation Artificial Neural Network Operator for Incomplete Acquisition in Wireless Sensor Networks

The data space collected by a wireless sensor network (WSN) is the basis of data mining and data visualization. In the process of monitoring physical quantities with large time and space correlations, incomplete acquisition strategy with data interpolation can be adopted to reduce the deployment cost. To improve the performance of data interpolation in such a scenario, we proposed a robust data interpolation based on a back propagation artificial neural network operator. In this paper, a neural network learning operator is proposed based on the strong fault tolerance of artificial neural networks. The learning operator is trained by using the historical data of the data acquisition nodes of WSN and is transferred to estimate the value of physical quantities at the locations where sensors are not deployed. The experimental results show that our proposed method yields smaller interpolation error than the traditional inverse-distance-weighted interpolation (IDWI) method

A Self-Adaptive Progressive Support Selection Scheme for Collaborative Wideband Spectrum Sensing

The sampling rate of wideband spectrum sensing for sparse signals can be reduced by sub-Nyquist sampling with a Modulated Wideband Converter (MWC). In collaborative spectrum sensing, the fusion center recovers the spectral support from observation and measurement matrices reported by a network of CRs, to improve the precision of spectrum sensing. However, the MWC has a very high hardware complexity due to its parallel structure; it sets a fixed threshold for a decision without considering the impact of noise intensity, and needs a priori information of signal sparsity order for signal support recovery. To address these shortcomings, we propose a progressive support selection based self-adaptive distributed MWC sensing scheme (PSS-SaDMWC). In the proposed scheme, the parallel hardware sensing channels are scattered on secondary users (SUs), and the PSS-SaDMWC scheme takes sparsity order estimation, noise intensity, and transmission loss into account in the fusion center. More importantly, the proposed scheme uses a support selection strategy based on a progressive operation to reduce missed detection probability under low SNR levels. Numerical simulations demonstrate that, compared with the traditional support selection schemes, our proposed scheme can achieve a higher support recovery success rate, lower sampling rate, and stronger time-varying support recovery ability without increasing hardware complexity