دورية أكاديمية
Deep Learning-Based Portable Image Analysis System for Real-Time Detection of Vespa velutina
| العنوان: | Deep Learning-Based Portable Image Analysis System for Real-Time Detection of Vespa velutina |
|---|---|
| المؤلفون: | Moon-Seok Jeon, Yuseok Jeong, Jaesu Lee, Seung-Hwa Yu, Su-bae Kim, Dongwon Kim, Kyoung-Chul Kim, Siyoung Lee, Chang-Woo Lee, Inchan Choi |
| المصدر: | Applied Sciences, Vol 13, Iss 13, p 7414 (2023) |
| بيانات النشر: | MDPI AG, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Biology (General) LCC:Physics LCC:Chemistry |
| مصطلحات موضوعية: | honeybee decline, Vespa velutina, deep learning, object detection, real-time streaming protocol, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999 |
| الوصف: | Honeybees pollinate over 75% of the total food resources produced annually, and they produce valuable hive products, such as bee pollen, propolis, and royal jelly. However, species such as the Asian hornet (Vespa velutina) feed on more than 85% of honeybees, causing a decline in their population and considerable damage to beekeepers in Korea. To prevent damage to honeybees, a portable real-time monitoring system was developed that detects V. velutina individuals and notifies users of their presence. This system was designed with a focus on portability and ease of installation, as V. velutina can be found in various areas of apiary sites. To detect V. velutina, an improved convolutional neural network YOLOv5s was trained on 1960 high-resolution (3840×2160) image data. At the confidence threshold of ≥0.600 and intersection over the union of ≥0.500, the performance of the system in terms of detection accuracy, precision, recall, F1 score, and mean average precision was high. A distance-based performance comparison showed that the system was able to detect V. velutina individuals while monitoring three beehives. During a field test of monitoring three beehives, the system could detect 83.3% of V. velutina during their hunting activities and send alarms to registered mobile application users. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2076-3417 |
| العلاقة: | https://www.mdpi.com/2076-3417/13/13/7414Test; https://doaj.org/toc/2076-3417Test |
| DOI: | 10.3390/app13137414 |
| الوصول الحر: | https://doaj.org/article/9b0b6427777d4edba65050a534373b66Test |
| رقم الانضمام: | edsdoj.9b0b6427777d4edba65050a534373b66 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20763417 |
|---|---|
| DOI: | 10.3390/app13137414 |