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The rapid and effective identification of leaf diseases of woody fruit plants can help fruit farmers prevent and cure diseases in time to improve fruit quality and minimize economic losses, which is of great significance to fruit planting. In recent years, deep learning has shown its unique advantages in image recognition. This paper proposes a new type of network based on deep learning image recognition method to recognize leaf diseases of woody fruit plants. The network merges the output of the convolutional layer of ResNet101 and VGG19 to improve the feature extraction ability of the entire model. It uses the transfer learning method to partially load the trained network weights, reducing model training parameters and training time. In addition, an attention mechanism is added to improve the efficiency of network information acquisition. Meanwhile, dropout, L2 regularization, and LN are used to prevent over-fitting, accelerate convergence, and improve the network's generalization ability. The experimental results show that the overall accuracy of woody fruit plant leaf diseases identification based on the model proposed in this paper is 86.41%. Compared with the classic ResNet101, the accuracy is improved by 1.71%, and the model parameters are reduced by 96.63%. Moreover, compared with the classic VGG19 network, the accuracy is improved by 2.08%, and the model parameters are reduced by 96.42%. After data set balancing, the overall identification accuracy of woody fruit plant leaf diseases based on the model proposed in this paper can reach 86.73%. [ABSTRACT FROM AUTHOR]

Insulator determines the insulation level and power supply reliability of transmission lines. The traditional operation and maintenance method of insulators has a large workload. This paper presents an insulator recognition and fault diagnosis system based on image recognition and machine learning. Firstly, the composite insulators in complex backgrounds have been identified by Faster RCNN algorithm, which helps to extract the image of insulators by drone shot. Then, the cracking of umbrella skirts has been carried out by means of image processing. Also, the contamination of composite insulator umbrella skirts is also identified. The appropriate feature quantity by Fisher's discrimination is recommended, and the insulator contamination level has been identified by S component. Lastly, analyzing the infrared image of the insulator provides the basis for the normalization of insulator temperature rise detection results in different environments. Insulator image autonomous recognition and defect intelligent detection, which is based on deep reinforcement learning, is helpful for the operation, maintenance and repair of line insulators. [ABSTRACT FROM AUTHOR]

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research. [ABSTRACT FROM AUTHOR]

Data classification in presence of noise can lead to much worse results than expected for pure patterns. In this paper we investigate this problem in the case of deep convolutional neural networks in order to propose solutions that can mitigate influence of noise. The main contributions presented in this paper are experimental examination of influence of different types of noise on the convolutional neural network, proposition of a deep neural network operating as a denoiser, investigation of a deep network training with noise contaminated patterns, and finally an analysis of noise addition during the training process of a deep network as a form of regularization. Our main findings are construction of the deep network based denoising filter which outperforms state-of-the-art solutions, as well as proposition of a practical method of deep neural network training with noisy patterns for improvement against the noisy test patterns. All results are underpinned by experiments which show high efficacy and possibly broad applications of the proposed solutions. [ABSTRACT FROM AUTHOR]

To monitor the scene anomaly in real-time through video and image and identify the emergencies, try to respond quickly at the beginning of the emergency and reduce the loss. This paper mainly focuses on the realization of the image recognition system for the anomalous characteristics of tourism emergencies. The problem is to study the number of people in the scenic spot based on scenic spot monitoring. The video-based population anomaly monitoring method has improved the AUC index of the W-SFM method by 0.423, and the AUC has increased by 0.0844 compared with the optical flow method; Degree-enhanced algorithm (BCOF), by grasping the micro-blog data related to the scenic spot, comprehensively predicts the overall comfort of the current tourists in the scenic spot, and establishes a tourist state expression model. Compared with the BN algorithm and the NEG algorithm, the BCOF algorithm is the accuracy and the recall rate of tourists in the scenic spots was improved by 14% and 18% respectively. The image recognition system of tourism emergency anomaly was established, and the early warning model of tourism emergency based on group intelligence perception was used to implement early warning on scenic spots. Monitoring, can achieve an overall accuracy of 83.33%, the model has a strong predictive ability, and achieves a scenic spot Real-time monitoring of events. [ABSTRACT FROM AUTHOR]