|
|
|
|
|
|
Tomato Leaf Liriomyza Sativae Blanchard Pest Detection Based on Hyperspectral Technology |
LI Cui-ling1, 2, JIANG Kai1, 2, MA Wei1, 2, WANG Xiu1, 2*, MENG Zhi-jun1, 2, ZHAO Xue-guan1, 2, SONG Jian1, 2 |
1. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China |
|
|
Abstract Tomato yield and farmers’ economic benefits will decrease when insect pest occurs in the growth of tomato plants. This study used hyperspectral technology combined with chemometrics methods to realize fast identification of tomato leaf LiriomyzaSativae Blanchard pest. A simple hyperspectral imaging system was developed, including a light source unit, and hyperspectral image acquisition unit and a data processing unit, and hyperspectral images of tomato leaves were collected through this system. Hyperspectral images were calibrated and spectral information was extracted from each image. Spectral angle mapping (SAM) analysis method and spectrum red edge parameters discriminant analysis (DA) method were adopted to identify tomato leaf Liriomyza Sativae Blanchard pest respectively. In the SAM analysis, normalization algorithm was utilized to preprocess hyperspectral data so as to eliminate redundant information in hyperspectral data and increase the differences between samples. Discriminant effects of tomato leaf pest were compared when different reflective spectrums of tomato leaf samples were used as test spectrums. It was found that when regarding the average reflectance spectrum of 100 tomato leaves infected by Liriomyzasativae Blanchard pest as the test spectrum, the overall recognition accuracy was higher, reaching to 96.5%. In spectrum red edge parameters discriminant analysis, 6 kinds of red edge information that red edge position, red edge amplitude, minimum amplitude, red edge area, location of minimum chlorophyll absorption, and the ratio of red edge amplitude to minimum amplitude were extracted from tomato leaves’ spectral data. Discriminant analysis method was used to develop discriminant model of tomato leaf LiriomyzaSativae Blanchard pest, discriminant effects of distance discriminant analysis, Fisher discriminant analysis, and Bayes discriminant analysis were compared. Comparison results indicated that Fisher discriminant analysis generated the best discriminant effect. The discriminant accuracy was 96.0% for validation set, while distance discriminant analysis produced the worst discriminant effect, with 88.0% discriminant accuracy. Research results showed that using hyperspectral technology to identify Liriomyza sativae Blanchard pest was feasible.
|
Received: 2017-02-10
Accepted: 2017-06-28
|
|
Corresponding Authors:
WANG Xiu
E-mail: wangx@nercita.org.cn
|
|
[1] CHANG Ya-wen, SHEN Yuan, DONG Chang-sheng, et al(常亚文,沈 媛,董长生,等). Chinese Journal of Applied Entomology(应用昆虫学报), 2016, 53(4): 884.
[2] ZHANG Jin-yu, GE Jin, WEI Jia-ning(张金钰,葛 瑨,魏佳宁). Chinese Journal of Applied Entomology(应用昆虫学报), 2015, 52(1): 184.
[3] Lausch A, Salbach C, Schmidt A, et al. Ecological Modelling, 2015, 295(8): 123.
[4] Castro A I D, Ehsani R, Ploetz R, et al. Remote Sensing of Environment, 2015, 171(2): 33.
[5] Xie Chuanqi, Yang Ce, He Yong. Computers and Electronics in Agriculture, 2017, 135: 154.
[6] LIU Wan-jun, YANG Xiu-hong, QU Hai-cheng, et al(刘万军,杨秀红,曲海成,等). Journal of Computer Applications(计算机应用), 2015, 35(3): 844.
[7] CHEN Hua, ZHAO Xin-min, TAN Qiao-lai, et al(陈 华,赵新民,谭乔来,等). Chinese Journal of Medical Imaging Technology(中国医学影像技术), 2016, 32(11): 1757.
[8] XIANG Shi-yao,XING Hui-min,XU Dong-jing(相诗尧,邢会敏,徐东晶). Science of Surveying and Mapping(测绘科学), 2016, (6): 1.
[9] TAN Chao, DAI Bo, LIU Hua-rong, et al(谭 超,戴 波,刘华戎,等). Food Science(食品科学), 2016, 37(7): 62.
[10] WEN Chang-ping, BAI Yin-yong, ZENG Juan-juan, et al(文畅平,白银涌,曾娟娟,等). Chinese Journal of Grassland(中国草地学报), 2016, 38(3): 50. |
[1] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
[2] |
ZHANG Yue1, 3, ZHOU Jun-hui1, WANG Si-man1, WANG You-you1, ZHANG Yun-hao2, ZHAO Shuai2, LIU Shu-yang2*, YANG Jian1*. Identification of Xinhui Citri Reticulatae Pericarpium of Different Aging Years Based on Visible-Near Infrared Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3286-3292. |
[3] |
ZHANG Zi-hao1, GUO Fei3, 4, WU Kun-ze1, YANG Xin-yu2, XU Zhen1*. Performance Evaluation of the Deep Forest 2021 (DF21) Model in
Retrieving Soil Cadmium Concentration Using Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2638-2643. |
[4] |
LI Bin, SU Cheng-tao, YIN Hai, LIU Yan-de*. Hyperspectral Imaging Technology Combined With Machine Learning for Detection of Moldy Rice[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2391-2396. |
[5] |
CHEN Xiao-li1, LI You-li1, LI Wei3, WANG Li-chun1, GUO Wen-zhong1, 2*. Effects of Red and Blue LED Lighting Modes on Spectral Characteristics and Coloring of Tomato Fruit[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1809-1814. |
[6] |
WANG Dong1, 2, FENG Hai-zhi3, LI Long3, HAN Ping1, 2*. Compare of the Quantitative Models of SSC in Tomato by Two Types of NIR Spectrometers[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1351-1357. |
[7] |
WANG Shao-yan1, CHEN Zhi-fei2, LUO Yang1, JIAN Chun-xia1, ZHOU Jun-jie3, JIN Yuan1, XU Pei-dan3, LEI Si-yue3, XU Bing-cheng1, 4*. Study on Nutrient Content of Bothriochloa Ischaemum Community in the Loess Hilly-Gully Region Based on Spectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1612-1621. |
[8] |
CUI Tian-yu1, LU Zhong-ling1, 2, XUE Lin3, WAN Shi-qi1, 2, ZHAO Ke-xin1, 2, WANG Hai-hua1, 2*. Research on the Rapid Detection Model of Tomato Sugar Based on
Near-Infrared Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1218-1224. |
[9] |
HE Lu1, WAN Li2, GAO Hui-yi2*. Recognition of Drought Stress in Tomato Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 724-730. |
[10] |
JIA Meng-meng, YIN Yong*, YU Hui-chun, YUAN Yun-xia, WANG Zhi-hao. Hyperspectral Imaging Combined With Feature Wavelength Screening for Monitoring the Quality Change of Tomato During Storage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 969-975. |
[11] |
WANG Ren-jie1, 2, FENG Peng1*, YANG Xing3, AN Le3, HUANG Pan1, LUO Yan1, HE Peng1, TANG Bin1, 2*. A Denoising Algorithm for Ultraviolet-Visible Spectrum Based on
CEEMDAN and Dual-Tree Complex Wavelet Transform[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 976-983. |
[12] |
HU Zheng1, ZHANG Yan1, 2*. Effect of Dimensionality Reduction and Noise Reduction on Hyperspectral Recognition During Incubation Period of Tomato Early Blight[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 744-752. |
[13] |
KONG De-ming1, CUI Yao-yao2, 3, ZHONG Mei-yu2, MA Qin-yong2, KONG Ling-fu2. Study on Identification Seawater Submersible Oil Based on Total
Synchronous Fluorescence Spectroscopy Combined With
High-Order Tensor Feature Extraction Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 62-69. |
[14] |
OUYANG Ai-guo, YU Bin, HU Jun, LIN Tong-zheng, LIU Yan-de. Grade Evaluation of Grain Size in High-Speed Railway Wheel Steel Based on Laser-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3428-3434. |
[15] |
FENG Hai-kuan1, 2, TAO Hui-lin1, ZHAO Yu1, YANG Fu-qin3, FAN Yi-guang1, YANG Gui-jun1*. Estimation of Chlorophyll Content in Winter Wheat Based on UAV Hyperspectral[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3575-3580. |
|
|
|
|