Microexpressions have extremely high due value in national security, public safety, medical, and other fields. However, microexpressions have characteristics that are obviously different from macroexpressions, such as short duration and weak changes, which greatly increase the difficulty of microexpression recognition work. In this paper, we propose a microexpression recognition method based on multimodal fusion through a comparative study of traditional microexpression recognition algorithms such as LBP algorithm and CNN and LSTM deep learning algorithms. The method couples the separate microexpression image information with the corresponding body temperature information to establish a multimodal fusion microexpression database. This paper firstly introduces how to build a multimodal fusion microexpression database in a laboratory environment, secondly compares the recognition accuracy of LBP, LSTM, and CNN + LSTM networks for microexpressions, and finally selects the superior CNN + LSTM network in the comparison results for model training and testing on the test set under separate microexpression database and multimodal fusion database. The experimental results show that a microexpression recognition method based on multimodal fusion designed in this paper is more accurate than unimodal recognition in multimodal recognition after feature fusion, and its recognition rate reaches 75.1%, which proves that the method is feasible and effective in improving microexpression recognition rate and has good practical value.

This paper proposes a novel bio-inspired metaheuristic algorithm called the Red-crowned Crane Optimization (RCO) algorithm. This algorithm is developed by mathematically modeling four habits of red-crowned cranes: dispersing for foraging, gathering for roosting, dancing, and escaping from danger. The foraging strategy is used to search unknown areas to ensure the exploration ability, and the roosting behavior prompts cranes to approach better positions, thereby enhancing the exploitation performance. The crane dancing strategy further balances the local and global search capabilities of the algorithm. Additionally, the introduction of the escaping mechanism effectively reduces the possibility of the algorithm falling into local optima. The RCO algorithm is compared with eight popular optimization algorithms on a large number of benchmark functions. The results show that the RCO algorithm can find better solutions for 74% of the CEC-2005 test functions and 50% of the CEC-2022 test functions. This algorithm has a fast convergence speed and high search accuracy on most functions, and it can handle high-dimensional problems. The Wilcoxon signed-rank test results demonstrate the significant superiority of the RCO algorithm over other algorithms. In addition, applications to eight practical engineering problems further demonstrate its ability to find near-optimal solutions.

Traditional agricultural cultivation is labor‐intensive and vulnerable to natural climate conditions, such as heavy rainfall and drought. Concerns over food safety have also brought attention to the growth of weeds and the misuse of agricultural chemicals, which can have a serious negative impact on crop growth and safety. We investigated the feasibility of the YoloX model in the field of agricultural weed identification to address the problem of weed handling in growing crops. In order to overcome the effects of climate and environment, we chose a purchased weed model for our study. We used a binocular vision camera for image acquisition and created a database containing 6,000 samples and enhanced the original database of 1,000 samples with data. In order to address the complex background of the weed images, the changing lighting environment of the binocular camera‐acquired images, and the noise interference, we performed histogram equalization, image denoising, and background processing on the dataset. These processing measures aim to improve the overall learning efficiency of the model in order to improve the accuracy of deep learning on weeds. Target detection platforms based on the TensorFlow and PyTorch frameworks were established, respectively, and the mainstream target detection models Faster R‐CNN and YoloX series target detection models were trained with the same dataset for comparative analysis using the longitudinal comparison method. The results show that the training under the PyTorch framework yields better models than the training under the TensorFlow framework. YoloX‐x has higher recognition accuracy, faster recognition speed, and more stable compared to the Faster R‐CNN model, with an average recognition rate of 97.07% and an average recognition time of 0.062 s. Moreover, the optimization of YoloX by incorporating an attention‐focusing mechanism resulted in an improved accuracy rate with a decrease in recognition time, with an average recognition rate of 97.70% and an average recognition time of 0.029 s.