| 基于无人机遥感的盛花期薇甘菊监测技术 |
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| 引用本文： | 李岩舟,覃锋,顾渝娟,韩阳春,田洪坤,乔曦.基于无人机遥感的盛花期薇甘菊监测技术[J].农业机械学报,2022,53(11):244-254. |
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| 作者姓名： | 李岩舟 覃锋 顾渝娟 韩阳春 田洪坤 乔曦 |
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| 作者单位： | 广西大学;广西大学;中国农业科学院（深圳）农业基因组研究所;广州海关技术中心;江阴海关;中国农业科学院（深圳）农业基因组研究所;东北大学 |
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| 基金项目： | 国家重点研发计划项目(2021YFD1400100、2021YFD1400101)、南宁市重点研发计划项目(20192065)、国家自然科学基金青年科学基金项目(31801804)、深圳市大鹏新区科技创新和产业发展专项资金项目(PT202001-06)和南京海关科研项目(2020KJ10) |
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| 摘 要： | 薇甘菊是世界十大有害杂草之一，其泛滥会对生态系统造成重大影响。建立一个高空间分辨率全域尺度的薇甘菊预警评估方法，是防治薇甘菊的关键手段之一。目前对薇甘菊的监测主要有人工踏查、卫星遥感监测，但前者效率低下而后者识别精度不够。以无人机为载体，通过采集待监测区域的薇甘菊彩色图像，应用Otsu-K-means、RGB、HSV色彩空间阈值分割算法以及K-means-RGB、K-means-HSV、K-means-RGB-HSV融合算法和MobileNetV3深度学习算法进行识别，采用召回率、精确率和均衡平均数F1值共3个评价指标对识别结果进行评价。实验结果表明K-means-RGB-HSV算法对盛花期薇甘菊的整体识别效果最佳。在此基础上，基于识别结果应用模糊层次分析法以及盖度公式，初步建立了薇甘菊的预警评估方法，划分了5个薇甘菊入侵危害等级，可根据所需监测精度的不同，设置不同尺寸的网格和辐射半径，绘制出薇甘菊入侵的精准分布热力图，能够清晰准确地体现不同区域的入侵薇甘菊的危害程度。在厘米级分辨率精度下，实现了基于无人机遥感的盛花期薇甘菊精准监测，为薇甘菊入侵的监测、预警和精准防治提供了有力支撑。
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| 关 键 词： | 薇甘菊 监测 无人机遥感 聚类分析 模糊层次分析法 |
| 收稿时间： | 2021/12/10 0:00:00 |
Monitoring Technology of Mikania micrantha in Flowering Period Based on UAV Remote Sensing |
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| LI Yanzhou,QIN Feng,GU Yujuan,HAN Yangchun,TIAN Hongkun,QIAO Xi.Monitoring Technology of Mikania micrantha in Flowering Period Based on UAV Remote Sensing[J].Transactions of the Chinese Society of Agricultural Machinery,2022,53(11):244-254. |
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| Authors: | LI Yanzhou QIN Feng GU Yujuan HAN Yangchun TIAN Hongkun QIAO Xi |
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| Institution: | Guangxi University;Guangxi University;Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences;Guangzhou Customs Districk Technology Center;Jiangyin Customs;Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences;Northeastern University |
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| Abstract: | Mikania micrantha is one of the top ten harmful weeds in the world, and its flooding will have a great impact on the ecosystem. Establishing a high spatial resolution and global scale early warning and assessment method for Mikania micrantha is one of the key measures to control Mikania micrantha. At present, Mikania micrantha is mainly monitored by manual survey and satellite remote sensing, but the former is inefficient and the latter is not accurate enough. Unmanned aerial vehicle (UAV) was used as the carrier to collect Mikania micrantha color images in the area to be monitored, the Otsu-K-means, RGB, HSV color space threshold segmentation algorithm and K-means-RGB, K-means-HSV, K-means-RGB-HSV fusion algorithm and MobileNetV3 deep learning algorithm were used for recognition. The recognition results were evaluated by three evaluation indexes: recall rate, accuracy rate and average F1-score value. The experimental results showed that K-means-RGB-HSV algorithm had the best overall recognition effect on Mikania micrantha in full bloom. On this basis, based on the recognition results, an early warning evaluation system of Mikania micrantha was constructed by applying fuzzy analytic hierarchy process and coverage formula, and five Mikania micrantha invasion hazard grades were divided. According to the different monitoring accuracies, grids with different sizes and radiation radius were set, and the accurate distribution heat map of Mikania micrantha invasion was drawn, which could clearly and accurately reflect the harm degree of Mikania micrantha invasion in different areas. Accurate monitoring of Mikania micrantha in full bloom based on UAV remote sensing was achieved with centimeter-level resolution accuracy, which provided strong support for monitoring, early warning and accurate prevention of Mikania micrantha invasion. |
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| Keywords: | Mikania micrantha monitoring UAV remote sensing cluster analysis fuzzy analytic hierarchy process |
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