Detection of Pest Degree of Phyllostachys Chinese With Hyperspectral Data
ZHENG Bei-jun1, 2, 3, CHEN Yun-zhi1, 2, 3*, LI Kai1, 2, 3, WANG Xiao-qin1, 2, 3, XU Zhang-hua1, 2, 4, HUANG Xu-ying5, HU Xin-yu4
1. Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350108, China
2. National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou 350108, China
3. Academy of Digital China (Fujian), Fuzhou 350108, China
4. School of Environmental and Safety Engineering, Fuzhou University, Fuzhou 350108, China
5. International Institute for Earth System Science, Nanjing University, Nanjing 210093, China
Abstract：The detection of insect pests of Phyllostachys edulis plays a vital role in the growth of bamboo and the development of the bamboo industry. Based on the relationship between the hyperspectral canopy spectrum information and the pest degree of Phyllostachys edulis, the characteristic wavelengths, indices, and spectral parameters closely related to the pests in the canopy spectrum were extracted, and Fisher’s discriminant analysis method was used to establish Phyllostachys edulis Pest degree detection model. Here are the wavelengths at 400～508, 586～693, 724～900 nm of the original spectrum, and the envelope curve to remove the characteristic wavelengths between 400～756 nm of the spectrum, 9 of canopy spectrum vegetation indices and 7 characteristic spectral parameters of the canopy are used as independent variables of the Fisher discriminant function to construct the discriminant function. Collected 300 groups of Phyllostachys pubescens leaf pest sample data, and randomly divided them into 210 modeling sets and 90 verification sets. According to the detection accuracy, Kappa coefficient and determination coefficient R2 as the test standards, the effect of the established discriminant function is evaluated and compared. The results show that the inspection accuracy of the Fisher discriminant function established by the original spectrum, de-envelope spectrum, canopy index, and spectral parameters as independent variables are 84.4%, 81.1%, 79.7%, 78.7%, respectively. The inspection accuracy of Kappa coefficient is 0.79, 0.74, 0.74, 0.76, and R2 is: 0.89, 0.88, 0.88, 0.85, respectively. It can be seen that the function established by the Fisher discriminant analysis model has a good ability to detect the degree of pests of the Phyllostachys edulis, and the discriminant function established based on the original spectrum of the canopy has the best detection effect. The discriminant function established based on the original spectrum of the canopy of the hyperspectral data was used to detect the pest degree of Phyllostachys edulis in Yangmen and Tulong Village in Wufang Village, Dagan Town, Shunchang County, Fujian Province. The test result is that the bamboo forests in the two sample areas of Shanghu are mainly healthy, and the pest degree of the two sample areas of Yangmen is mainly moderate and severe. Therefore, based on UAV hyperspectral remote sensing, it is feasible for large-area detection of Phyllostachys edulis pests. The method and results can provide a reference for the exploration of pest detection and contribute theoretical support for pest detection based on canopy remote sensing.
郑蓓君，陈芸芝，李 凯，汪小钦，许章华，黄旭影，胡新宇. 高光谱数据的刚竹毒蛾虫害程度检测[J]. 光谱学与光谱分析, 2021, 41(10): 3200-3207.
ZHENG Bei-jun, CHEN Yun-zhi, LI Kai, WANG Xiao-qin, XU Zhang-hua, HUANG Xu-ying, HU Xin-yu. Detection of Pest Degree of Phyllostachys Chinese With Hyperspectral Data. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3200-3207.
[1] HUANG Xu-ying, XU Zhang-hua, LIN Lu, et al(黄旭影, 许章华, 林 璐, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(3): 857.
[2] XIE Xiang, WANG Shi-hua, WANG Qiao-yan, et al(谢 翔, 王仕华, 王巧燕, 等). Forest Inventory and Planning(林业调查规划), 2020, 45(3): 65.
[3] QI Xing-lan, CAO Zu-ning, LIU Jian, et al(亓兴兰, 曹祖宁, 刘 健, 等). Forest Resources Management(林业资源管理), 2020，(2): 181.
[4] Alves T M, Moon R D, MacRae I V, et al. Pest Management Science, 2019, 75(4): 942.
[5] LUO Qing-qing, HUANG Tie-cheng, CHEN Shu-jiang, et al(罗青青, 黄铁成, 陈蜀江, 等). Jiangsu Journal of Agricultural Sciences(江苏农业学报), 2019, 35(4): 798.
[6] Iordache M, Mantas V, Baltazar E, et al. Remote Sensing, 2020, 12(14): 2280.
[7] Marston Z P D, Cira T M, Hodgson E W, et al. Journal of Economic Entomology, 2020, 113(2): 779.
[8] ZHANG Hui, LIU Wan-jun, LÜ Huan-huan, et al(张 辉, 刘万军, 吕欢欢, 等). Pattern Recognition and Artificial Intelligence(模式识别与人工智能), 2019, 32(7): 624.
[9] ZHAO Yong-jun, XIE Yang-sheng, WANG Jian-jun, et al(赵勇钧, 谢阳生, 王建军, 等). Journal of Central South University of Forestry & Technology(中南林业科技大学学报), 2019, 39(7): 100.
[10] MENG Xiang-tian, BAO Yi-lin, LIU Huan-jun, et al(孟祥添, 鲍依临, 刘焕军, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2020, 36(16): 231.
[11] GAO Kai-xuan, JIAO Hai-ming, WANG Xin-chuang (高凯旋, 焦海明, 王新闯). Remote Sensing for Land & Resources(国土资源遥感), 2020，(4): 63.
[12] LIU Yue-han, LI Song-song, ZHENG Wei, et al(刘岳含, 黎松松, 郑 伟, 等). Chinese Journal of Grassland(中国草地学报), 2020, 42(2): 83.
[13] ZHOU Xiao, BAO Yun-xuan, WANG Lin, et al(周 晓, 包云轩, 王 琳, 等). Chinese Journal of Agrometeorology(中国农业气象), 2020, 41(3): 173.
[14] XIAO Lu-jie, YANG Wu-de, FENG Mei-chen, et al(肖璐洁, 杨武德, 冯美臣, 等). Journal of Shanxi Agricultural University·Natural Science Edition(山西农业大学学报·自然科学版), 2020: 1.
[15] DU Jing-yuan, GE Hong-li, LU Wei, et al(杜靖媛, 葛宏立, 路 伟, 等). Journal of Southwest Forestry University·Natural Sciences(西南林业大学学报·自然科学), 2017, 37(4): 175.
