Academic Journal of Computing & Information Science, 2024, 7(6); doi: 10.25236/AJCIS.2024.070602.
Junyi Wang1, Yinghuai Yu1, Minhua Ye2
1School of Mathematics and Computer, Guangdong Ocean University, Zhanjiang, 524088, China
2College of Ocean Engineering and Energy, Guangdong Ocean University, Zhanjiang, 524088, China
Aiming at the low efficiency of traditional Chinese herbal medicine pest control technology, an intelligent system for pest control of Chinese herbal medicine was proposed. The deep learning model was deployed to an intelligent UAV to realize real-time dynamic identification of diseases and pests, and the RTK module was used to connect the software end to realize data interconnection and real-time analysis of pest identification results, and then return to intelligent planning of management routes. To create a highly automated system for pest identification and management. The average pest identification accuracy of the system model reached 96.72%, covering 328 species of pests and diseases, and the software video stream return frame rate was no less than 30 frames, effectively improving the efficiency of pest and disease management.
Chinese medicinal materials, Pests and diseases, Intelligent recognition, Prevention and cure
Junyi Wang, Yinghuai Yu, Minhua Ye. Intelligent pest and disease control system for traditional Chinese medicine cultivation industry. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 6: 7-12. https://doi.org/10.25236/AJCIS.2024.070602.
[1] Sun Rongze. Combining UAV and intelligent robot technology, BLUERIVER farming + digital automation TECHNOLOGY to realize the agriculture industry [EB/OL]. (2017-3-15) [2024-04-30]. https://www. 36kr.com/p/1721414270977.
[2] Tao S H. Research on microscopic image recognition of Chinese medicinal powder based on texture features [D]. North China University of Technology, 2024.
[3] Pan Wei, Wei Chao, Qian Chunyu, et al. Improved YOLOv8s model for small target detection from UAV perspective [J/OL]. Computer engineering and application: 1-10. [2024-05-01] http://kns.cnki. net/k- CMS/detail / 11.2127 TP. 20240308.1652.008. HTML.
[4] Interface news. Is the big era of food AI at hand? [EB/OL]. (2023-03-13)[2024-04-30]. https://www.jiemian.com/article/9055387.html.
[5] Chen X. Recognition and application of MobileNet V3 apple leaf disease based on multi-scale [D]. Anhui Agricultural University, 2024.
[6] Zeng T H. Real-time detection algorithm and mobile deployment of lightweight tomato based on YOLO [D]. Fujian Agriculture and Forestry University, 2023.