Image Recognition of Male Oilseed Rape (Brassica napus) Plants Based on Convolutional Neural Network for UAAS Navigation Applications on Supplementary Pollination and Aerial Spraying

To ensure the hybrid oilseed rape (OSR, Brassica napus) seed production, two important things are necessary, the stamen sterility on the female OSR plants and the effective pollen spread onto the pistil from the OSR male plants to the OSR female plants. The unmanned agricultural aerial system (UAAS) has developed rapidly in China. It has been used on supplementary pollination and aerial spraying during the hybrid OSR seed production. This study developed a new method to rapidly recognize the male OSR plants and extract the row center line for supporting the UAAS navigation. A male OSR plant recognition model was constructed based on the convolutional neural network (CNN). The sequence images of male OSR plants were extracted, the feature regions and points were obtained from the images through morphological and boundary process methods and horizontal segmentation, respectively. The male OSR plant image recognition accuracies of different CNN structures and segmentation sizes were discussed. The male OSR plant row center lines were fitted using the least-squares method (LSM) and Hough transform. The results showed that the segmentation algorithm could segment the male OSR plants from the complex background. The highest average recognition accuracy was 93.54%, and the minimum loss function value was 0.2059 with three convolutional layers, one fully connected layer, and a segmentation size of 40 pix × 40 pix. The LSM is better for center line fitting. The average recognition model accuracies of original input images were 98% and 94%, and the average root mean square errors (RMSE) of angle were 3.22° and 1.36° under cloudy day and sunny day lighting conditions, respectively. The results demonstrate the potential of using digital imaging technology to recognize the male OSR plant row for UAAS visual navigation on the applications of hybrid OSR supplementary pollination and aerial spraying, which would be a meaningful supplement in precision agriculture.

WiPg: Contactless Action Recognition Using Ambient Wi-Fi Signals

Motion recognition has a wide range of applications at present. Recently, motion recognition by analyzing the channel state information (CSI) in Wi-Fi packets has been favored by more and more scholars. Because CSI collected in the wireless signal environment of human activity usually carries a large amount of human-related information, the motion-recognition model trained for a specific person usually does not work well in predicting another person’s motion. To deal with the difference, we propose a personnel-independent action-recognition model called WiPg, which is built by convolutional neural network (CNN) and generative adversarial network (GAN). According to CSI data of 14 yoga movements of 10 experimenters with different body types, model training and testing were carried out, and the recognition results, independent of bod type, were obtained. The experimental results show that the average correct rate of WiPg can reach 92.7% for recognition of the 14 yoga poses, and WiPg realizes “cross-personnel” movement recognition with excellent recognition performance.

Chemical Pattern Recognition for Quality Analysis of Lonicerae Japonicae Flos and Lonicerae Flos Based on Ultra-High Performance Liquid Chromatography and Anti-SARS-CoV2 Main Protease Activity

Lonicerae japonicae flos (L. japonicae flos, Lonicera japonica Thunb.) is one of the most commonly prescribed botanical drugs in the treatment or prevention of corona virus disease 2019. However, L. japonicae flos is often confused or adulterated with Lonicerae flos (L. flos, Lonicera macrantha (D.Don) Spreng., Shanyinhua in Chinese). The anti-SARS-CoV2 activity and related differentiation method of L. japonicae flos and L. flos have not been documented. In this study, we established a chemical pattern recognition model for quality analysis of L. japonicae flos and L. flos based on ultra-high performance liquid chromatography (UHPLC) and anti-SARS-CoV2 activity. Firstly, chemical data of 59 batches of L. japonicae flos and L. flos were obtained by UHPLC, and partial least squares-discriminant analysis was applied to extract the components that lead to classification. Next, anti-SARS-CoV2 activity was measured and bioactive components were acquired by spectrum-effect relationship analysis. Finally, characteristic components were explored by overlapping feature extracted components and bioactive components. Accordingly, eleven characteristic components were successfully selected, identified, quantified and could be recommended as quality control marker. In addition, chemical pattern recognition model based on these eleven components was established to effectively discriminate L. japonicae flos and L. flos. In sum, the demonstrated strategy provided effective and highly feasible tool for quality assessment of natural products, and offer reference for the quality standard setting.

Abnormal Driving Behavior Recognition Method Based on Smart Phone Sensor and CNN-LSTM

Accurate identification of abnormal driving behavior is very important to improve driver safety. Aiming at the problem that threshold or traditional machine learning methods are mostly used in existing studies, it is difficult to accurately identify abnormal driving behavior of vehicles, a method of abnormal driving behavior recognition based on smartphone sensor data and convolutional neural network (CNN) combined with long and short-term memory (LSTM) was proposed. Smartphone sensors are used to collect vehicle driving data, and data sets of various driving behaviors are constructed by preprocessing the data. A recognition model based on a convolutional neural network combined with a long short-term memory network was constructed to extract depth features from data sets and recognize abnormal driving behaviors. The test results show that the accuracy of the model based on CNN-LSTM can reach 95.22%, and the performance indexes can reach more than 94%. Compared with the recognition model constructed only by CNN or LSTM, this model has higher recognition accuracy.