摘要
本文以林业松材线虫病识别为例,对某地开展基于无人机和人工智能技术的松材线虫病异常林木试验和研究。通过采集精度达到0.1m的可见光航拍数据,自行开发人工智能的语义分割和小目标识别框架,并基于该框架,设计和开发出适用于异常林木的深度学习的连接层和池化层。实验证明,识别准确率可以达到90%以上,速度达到人工的70倍。整体方案技术稳定可行,能够根本性解决异常林木快速识别问题。
In this paper, a case experimental study was made on technologies for identification of pinewood nematodiasis in abnormal forest trees at a sample plot. By collecting data from visible aerial photograph with 0.1-m resolution, a semantic segmentation and smallobject identification framework of artificial intelligence was independently developed. Based on the framework, the in-depth-learning connecting layer and pooling layer suitable for identifying abnormal forest trees were designed and developed. The experiment proved that the identification accuracy could reach above 90% and the identification speed could be 70 times faster than that of the artificial identification. The overall plan was technologically stable and workable and could be a fundamental solution to rapid identification of abnormal forest trees.
作者
吕明站
朱子魁
Lü Mingzhan;ZHU Zikui(Beijing Luckywing Information Technologies Co. Ltd., Beijing 100191, China)
出处
《安徽林业科技》
2019年第2期13-18,共6页
Anhui Forestry Science and Technology
关键词
无人机
人工智能
异常林木
松材线虫病
Unmanned aerial vehicle
Artificial intelligence
Abnormal forest trees
Pinewood nematodosis
