Improved YOLO v5s-based detection method for external defects in potato

Currently, potato defect sorting primarily relies on manual labor, which is not only inefficient but also prone to bias. Although automated sorting systems offer a potential solution by integrating potato detection models, real-time performance remains challenging due to the need to balance high accuracy and speed under limited resources. This study presents an enhanced version of the YOLO v5s model, named YOLO v5s-ours, specifically designed for real-time detection of potato defects. By integrating Coordinate Attention (CA), Adaptive Spatial Feature Fusion (ASFF), and Atrous Spatial Pyramid Pooling (ASPP) modules, the model significantly improves detection accuracy while maintaining computational efficiency. The model achieved 82.0% precision, 86.6% recall, 84.3% F1-Score and 85.1% mean average precision across six categories — healthy, greening, sprouting, scab, mechanical damage, and rot — marking improvements of 24.6%, 10.5%, 19.4%, and 13.7%, respectively, over the baseline model. Although memory usage increased from 13.7 MB to 23.3 MB and frame rate slightly decreased to 30.7 fps, the accuracy gains ensure the model’s suitability for practical applications. The research provides significant support for the development of automated potato sorting systems, advancing agricultural efficiency, particularly in real-time applications, by overcoming the limitations of traditional methods

Efficient Non-Destructive Detection for External Defects of Kiwifruit

External defects of kiwifruit seriously affect its added commercialization. To address the existing problems, kiwifruit external defects detection has a few methods for detecting multi-category defects and weak adaptability to complex images. In this study, we proposed ResNet combined with CBAM for the automatic detection of external defects in kiwifruit. The experiment first built an acquisition device to obtain high-quality images. The optimal fusion scheme of ResNet and CBAM was investigated, the network training parameters were optimized, and Adam was used to accelerate the convergence speed of the model. It was found that the average recognition accuracy of ResNet34 + CBAM for kiwifruit was 99.6%, and all evaluation metrics were greater than 99%. Meanwhile, the experiment selected AlexNet, VGG16, InceptionV3, ResNet34, and ResNet34 + CBAM for comparison. The results showed that the recognition accuracy of ResNet34 + CBAM was 7.9%, 12.7%, 11.8%, and 4.3% higher than that of AlexNet, VGG16, InceptionV3, and ResNet34, respectively. Therefore, it can be concluded that ResNet34 + CBAM has the advantages of high recognition accuracy and good stability for kiwifruit external defect sample detection. It provides a technical guarantee for online detection and sorting of kiwifruit and other fruit defects

Multi-sensor Array Based Fire Monitor for Cotton Pile

International audienceCotton is a strategic material for people’s livelihood, however, its fiber is most combustible in all kinds of natural fiber. To warn this risk, the mechanism of cotton pile smoke and fire was studied in this paper, and the monitoring and early warning indicators of the cotton pile fire were selected. The multi-sensor array was designed and to developed sense temperature, humidity and CO concentration. The fire monitoring device has the key modules of temperature sensor, humidity sensor, CO gas sensor, and PC center. A user interface was developed on LabVIEW to display and control data. Multi-sensor array based fire monitor can provide the data of temperature, humidity and CO concentration. It is essential to effectively measure abnormal or normal situation of the cotton pile, and can make early warning for the cotton fire and reduce the loss of cotton fire disaster