In view of the time-consuming and laborious manual picking and sorting of strawberries, the direct impact of image recognition accuracy on automatic picking and the rapid development of deep learning(DL), a Faster Regions with Convolutional Neural Network features (R-CNN) strawberry recognition method that combines Mixup data augmentation, a ResNet(Residual Network)50 backbone feature extraction network and a Soft-NMS (Non-Maximum Suppression) algorithm, named the MRS Faster R-CNN, is proposed. In this paper, the transfer learning backbone feature extraction network VGG (Visual Geometry Group) 16 and ResNet50 are compared, and the superior ResNet50 is selected as the backbone network of MRS Faster R-CNN. The data augmentation method of Mixup image fusion is used to improve the learning and generalization ability of the model. The redundant bboxes (bounding boxes) are removed through Soft-NMS to obtain the best region proposal. The freezing phase is added to the training process, effectively reducing the occupation of video memory and shortening the training time. After experimental verification, the optimized model improved the AP (Average Precision) values of mature and immature strawberries by 0.26% and 5.34%, respectively, and the P(Precision) values by 0.81% and 6.34%, respectively, compared to the original model (R Faster R-CNN). Therefore, the MRS Faster R-CNN model proposed in this paper has great potential in the field of strawberry recognition and maturity classification and improves the recognition rate of small fruit and overlapping occluded fruit, thus providing an excellent solution for mechanized picking and sorting.

In the process of wireless image transmission, there are a large number of interference signals, but the traditional interference signal recognition system is limited by various modulation modes, it is difficult to accurately identify the target signal, and the reliability of the system needs to be further improved. In order to solve this problem, a wireless image transmission interference signal recognition system based on deep learning is designed in this paper. In the hardware part, STM32F107VT and SI4463 are used to form a wireless controller to control the execution of each instruction. In the software part, aiming at the time‐domain characteristics of the interference signal, the feature vector of the interference signal is extracted. With the support of GAP‐CNN model, the interference signal is recognized through the training and learning of feature vector. The experimental results show that the packet loss rate of the designed system is less than 0.5%, the recognition performance is good, and the reliability of the system is improved.

Due to the difficulty in distinguishing transparent and white foreign fibers from seed cotton in RGB images and in order to improve the recognition ability of deep learning (DL) algorithms for white, transparent, and multi-class mixed foreign fibers with different sizes in seed cotton, this paper proposes a method of combining hyperspectral imaging technology with a multi-module joint hierarchical residue network (MJHResNet). Firstly, a series of preprocessing methods are performed on the hyperspectral image (HSI) to reduce the interference of noise. Secondly, a double-hierarchical residual (DHR) structure is designed, which can not only obtain multi-scale information, but also avoid gradient vanishing to some extent. After that, a squeeze-and-excitation network (SENet) is integrated to reduce redundant information, improve the expression of model features, and improve the accuracy of foreign fiber identification in seed cotton. Finally, by analyzing the experimental results with advanced classifiers, this method has significant advantages. The average accuracy is 98.71% and the overall accuracy is 99.28%. This method has great potential for application in the field of foreign fiber identification in seed cotton.

In this paper, we study the nonlinear optical bistability (OB) in a symmetrical multilayer structure. This structure is constructed by embedding a nonlinear three-dimensional Dirac semimetal (3D DSM) into a solution filled one-dimensional photonic crystal Fabry-Perot cavity. OB stems from the third order nonlinear conductivity of 3D DSM and the local field of resonance mode could enhance the nonlinearity and reduce the thresholds of OB. This structure achieves the tunability of OB due to that the transmittance could be modulated by the Fermi energy. OB threshold and threshold width could be remarkably reduced by increasing the Fermi energy. Besides, it is found that the OB curve depends heavily on the angle of incidence of the incoming light, the structural parameters of the Fabry-Perot cavity, and the position of 3D DSM inside the cavity. After parameter optimization, we obtained OB with a threshold of 106 V/m. We believe this simple structure provides a reference idea for realizing low threshold and tunable all optical switching devices. Keywords: Optical bistability, Dirac semimetal, Fabry-Perot cavity.

In order to improve the effectiveness of the analysis method of Internet addiction tendency, a multi factor weight model analysis method of Internet Addiction Tendency Based on machine learning method is proposed. Firstly, the research status of machine learning is analyzed, and a multi factor weight model of Internet addiction tendency is constructed; The deep belief network structure is designed based on machine learning method; The deep belief network is used to realize the weight sharing of Internet addiction tendency multi factors, effectively extract the convolution characteristics of Internet addiction tendency multi factors, and realize the analysis of Internet addiction tendency multi factor weight model. The experimental results show that the multi factor weight model of Internet Addiction Tendency Based on machine learning method can effectively improve the accuracy and effect of Internet addiction tendency analysis.