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MmNet: Identifying Mikania micrantha Kunth in the wild via a deep Convolutional Neural Network

文献类型： 外文期刊

作者： Qiao Xi 1 ; Li Yan-zhou 3 ; Su Guang-yuan 4 ; Tian Hong-kun 3 ; Zhang Shuo 5 ; Sun Zhong-yu 6 ; Yang Long 6 ; Wan Fang-ha 1 ;

作者机构： 1.Chinese Acad Agr Sci, Agr Genom Inst Shenzhen, Shenzhen 518120, Peoples R China

2.South China Agr Univ, Key Lab Integrated Pest Management Crops South Ch, Minist Agr & Rural Affairs, Guangzhou 510642, Peoples R China

3.Guangxi Univ, Coll Mech Engn, Nanning 530004, Peoples R China

4.Shaanxi Agr Machinery Appraisal Stn, Xian 710065, Peoples R China

5.Northwest A&F Univ, Coll Mech & Elect Engn, Yangling 712100, Shaanxi, Peoples R China

6.Guangdong Acad Sci, Guangzhou Inst Geog, Guangzhou 510070, Peoples R China

关键词： Mikania micrantha Kunth; invasive alien plant; image processing; deep learning

期刊名称：JOURNAL OF INTEGRATIVE AGRICULTURE （ 影响因子：2.848； 五年影响因子：2.979 ）

ISSN： 2095-3119

年卷期： 2020 年 19 卷 5 期

页码：

收录情况： SCI

摘要： Mikania micrantha Kunth is an invasive alien weed and known as a plant killer around the world. Accurately and rapidly identifying M. micrantha in the wild is important for monitoring its growth status, as this helps management officials to take the necessary steps to devise a comprehensive strategy to control the invasive weed in the identified area. However, this approach still mainly depends on satellite remote sensing and manual inspection. The cost is high and the accuracy rate and efficiency are low. We acquired color images of the monitoring area in the wild environment using an Unmanned Aerial Vehicle (UAV) and proposed a novel network -MmNet-based on a deep Convolutional Neural Network (CNN) to identify M. micrantha in the images. The network consists ofAlexNet Local Response Normalization (LRN), along with the GoogLeNet and continuous convolution of VGG inception models. After training and testing, the identification of 400 testing samples by MmNet is very good, with accuracy of 94.50% and time cost of 10.369 s. Moreover, in quantitative comparative analysis, the proposed MmNet not only has high accuracy and efficiency but also simple construction and outstanding repeatability. Compared with recently popular CNNs, MmNet is more suitable for the identification of M. micrantha in the wild. However, to meet the challenge of wild environments, more M. micrantha images need to be acquired for MmNet training. In addition, the classification labels need to be sorted in more detail. Altogether, this research provides some theoretical and scientific basis for the development of intelligent monitoring and early warning systems for M. micrantha and other invasive species.

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