The use of yellow sticky traps is a green pest control method that utilizes the pests’ attraction to the color yellow. The use of yellow sticky traps not only controls pest populations but also enables monitoring, offering a more economical and environmentally friendly alternative to pesticides. However, the small size and dense distribution of pests on yellow sticky traps lead to lower detection accuracy when using lightweight models. On the other hand, large models suffer from longer training times and deployment difficulties, posing challenges for pest detection in the field using edge computing platforms. To address these issues, this paper proposes a lightweight detection method, YOLO-YSTs, based on an improved YOLOv10n model. The method aims to balance pest detection accuracy and model size and has been validated on edge computing platforms. This model incorporates SPD-Conv convolutional modules, the iRMB inverted residual block attention mechanism, and the Inner-SIoU loss function to improve the YOLOv10n network architecture, ultimately addressing the issues of missed and false detections for small and overlapping targets while balancing model speed and accuracy. Experimental results show that the YOLO-YSTs model achieved precision, recall, mAP₅₀, and mAP_50–95 values of 83.2%, 83.2%, 86.8%, and 41.3%, respectively, on the yellow sticky trap dataset. The detection speed reached 139 FPS, with GFLOPs at only 8.8. Compared with the YOLOv10n model, the mAP₅₀ improved by 1.7%. Compared with other mainstream object detection models, YOLO-YSTs also achieved the best overall performance. Through improvements to the YOLOv10n model, the accuracy of pest detection on yellow sticky traps was effectively enhanced, and the model demonstrated good detection performance when deployed on edge mobile platforms. In conclusion, the proposed YOLO-YSTs model offers more balanced performance in the detection of pest images on yellow sticky traps. It performs well when deployed on edge mobile platforms, making it of significant importance for field pest monitoring and integrated pest management.

Invasive alien plants (IAPs) present a significant threat to ecosystems and agricultural production, necessitating rigorous monitoring and detection for effective management and control. To realize accurate and rapid detection of invasive alien plants in the wild, we proposed a rapid detection approach grounded in an advanced YOLOv9, referred to as YOLO-IAPs, which incorporated several key enhancements to YOLOv9, including replacing the down-sampling layers in the model’s backbone with a DynamicConv module, integrating a Triplet Attention mechanism into the model, and replacing the original CIoU with the MPDloU. These targeted enhancements collectively resulted in a substantial improvement in the model’s accuracy and robustness. Extensive training and testing on a self-constructed dataset demonstrated that the proposed model achieved an accuracy of 90.7%, with the corresponding recall, mAP50, and mAP50:95 measured at 84.3%, 91.2%, and 65.1%, and a detection speed of 72 FPS. Compared to the baseline, the proposed model showed increases of 0.2% in precision, 3.5% in recall, and 1.0% in mAP50. Additionally, YOLO-IAPs outperformed other state-of-the-art object detection models, including YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv10 series, Faster R-CNN, SSD, CenterNet, and RetinaNet, demonstrating superior detection capabilities. Ablation studies further confirmed that the proposed model was effective, contributing to the overall improvement in performance, which underscored its pre-eminence in the domain of invasive alien plant detection and offered a marked improvement in detection accuracy over traditional methodologies. The findings suggest that the proposed approach has the potential to advance the technological landscape of invasive plant monitoring.

Invasive plant species pose significant biodiversity and ecosystem threats. Real-time identification of invasive plants is a crucial prerequisite for early and timely prevention. While deep learning has shown promising results in plant recognition, the use of deep learning models often involve a large number of parameters and high data requirements for training. Unfortunately, the available data for various invasive plant species are often limited. To address this challenge, this study proposes a lightweight deep learning model called IPMCNet for the identification of multiple invasive plant species. IPMCNet attains high recognition accuracy even with limited data and exhibits strong generalizability. Simultaneously, by employing depth-wise separable convolutional kernels, splitting channels, and eliminating fully connected layer, the model’s parameter count is lower than that of some existing lightweight models. Additionally, the study explores the impact of different loss functions, and the insertion of various attention modules on the model’s accuracy. The experimental results reveal that, compared with eight other existing neural network models, IPMCNet achieves the highest classification accuracy of 94.52%. Furthermore, the findings suggest that focal loss is the most effective loss function. The performance of the six attention modules is suboptimal, and their insertion leads to a decrease in model accuracy.

Parkinson’s disease (PD), as the second most prevalent neurodegenerative disorder worldwide, impacts the quality of life for over 12 million patients. This study aims to enhance the accuracy of early diagnosis of PD through non-invasive methods, with the goal of enabling earlier intervention in the disease process. To this end, we constructed an open-field environment using flexible sensors under dark conditions, conducting experiments on a mouse model of Parkinson’s disease alongside a normal control group. By processing footprint images collected in the absence of light—employing numerical area summation for noise reduction, adaptive enhancement algorithms based on pixel values, and a high-accuracy Convolutional Neural Network algorithm. And integrating motion data analysis, we achieved effective fusion of footprint images and behavioral data. After comprehensive analysis using decision-level fusion techniques and a Naive Bayes classifier, the results showed that the average classification accuracy for PD mice reached 96.56% when employing a multimodal data fusion strategy, demonstrating a significant advantage over using a single image recognition technique. This research not only highlights the substantial potential of applying multimodal data fusion strategies in the early detection of Parkinson’s disease but also proves that such an approach can significantly improve diagnostic accuracy. The findings provide new insights into the early diagnosis of Parkinson’s disease and other neurodegenerative disorders.

To solve the problem that traditional seed recognition methods are not completely suitable for needle-shaped seeds, such as Bidens L., in agricultural production, this paper proposes a model construction idea that combines the advantages of deep residual models in extracting high-level abstract features with multiscale feature extraction fusion, taking into account the depth and width of the network. Based on this, a multiscale feature fusion deep residual network (MSFF-ResNet) is proposed, and image segmentation is performed before classification. The image segmentation is performed by a popular semantic segmentation method, U²Net, which accurately separates seeds from the background. The multiscale feature fusion network is a deep residual model based on a residual network of 34 layers (ResNet34), and it contains a multiscale feature fusion module and an attention mechanism. The multiscale feature fusion module is designed to extract features of different scales of needle-shaped seeds, while the attention mechanism is used to improve the ability to select features of our model so that the model can pay more attention to the key features. The results show that the average accuracy and average F1-score of the multiscale feature fusion deep residual network on the test set are 93.81% and 94.44%, respectively, and the numbers of floating-point operations per second (FLOPs) and parameters are 5.95 G and 6.15 M, respectively. Compared to other deep residual networks, the multiscale feature fusion deep residual network achieves the highest classification accuracy. Therefore, the network proposed in this paper can classify needle-shaped seeds efficiently and provide a reference for seed recognition in agriculture.