高光谱成像的多种类柑橘病虫药害叶片检测方法研究

doi:10.3964/j.issn.1000-0593(2022)08-2397-06

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Abstract Numerous kinds of diseases and insect pests affect citrus’ growth, but most of the current detection methods are for a single condition. It is important for the accurate application of pesticides in orchards and the healthy development of the citrus industry that the development of a detection method based on hyperspectral imaging and machine learning to achieve rapid and accurate detection of multiple pests and diseases on citrus leaves. Naturally onset citrus leaves in orchards were used as research objects, including normal citrus leaves (50 pieces), ulcer disease leaves (50 pieces), soot disease leaves (103 pieces), nutrient deficiency disease leaves (60 pieces), and red spider leaves (56 pieces) and herbicide damage leaves (85 pieces), hyperspectral data in the 350～1 050 nm band were collected. First-order derivation (1^stDer), multivariate scattering correction (MSC) and median filtering (MF) were used to preprocess the original (Origin) hyperspectral data, principal component analysis (PCA) and competitive adaptive weighting (CARS) algorithms were used to extract characteristic wavelengths from the prepossessed hyperspectral data. Characteristic wavelengths obtained by CARS were 10, 5, 12 and 10 respectively, and the 4 sets of characteristic wavelengths obtained by PCA were all 7, ranging in the 700～760 nm band. The limit gradient boosting tree (XGBoost) was used for the full band (FS), and the support vector machine (SVM) was used for the characteristic wavelength to establish a multi-classification model of citrus diseased leaves. The classification models established by XGBoost are Origin-FS-XGBoost, 1^stDer-FS-XGBoost, MSC-FS-XGBoost and MF-FS-XGBoost, and the overall classification accuracy (OA) obtained from the detection of 6 kinds of diseases and insect pests leaves was 94.32%, 93.60%, 95.98% and 96.56% respectively; the classification models established by SVM are Origin-CARS-SVM, 1^stDer-CARS-SVM, MSC-CARS-SVM, MF-CARS-SVM, Origin-PCA-SVM, 1^stDer-PCA-SVM, MSC-PCA-SVM and MF-PCA-SVM, model OA was 93.63%, 90.26%, 87.90%, 91.95%, 87.53%, 90.82%, 83.50% and 90.98% respectively. The experimental results demonstrate that the recognition rate of the XGBoost model with FS as input was better than the SVM model with characteristic wavelength as input. The OA of the MF-FS-XGBoost model was 96.56%, the Recall was 95.91%, and the model training time was 63 s. The overall performance was the best; the CARS-SVM modeling effect was better than PCA-SVM. After pre-processing by all three methods, the recognition rate of the CARS-SVM model was above 87%, and the recognition rate of the PCA-SVM model was above 83%. The results show that hyperspectral imaging technology combined with machine learning methods can classify and identify multiple species of citrus pests and diseases, providing a theoretical basis for rapid and non-destructive detection and control of citrus pests and diseases.

Key words：Hyperspectral imaging; Diseased citrus leaves; Characteristic wavelength extraction; XGBoost; SVM

Received: 2021-11-29 Accepted: 2022-02-25

ZTFLH:

O433.4

Cite this article:

WU Ye-lan,GUAN Hui-ning,LIAN Xiao-qin, et al. Study on Detection Method of Leaves With Various Citrus Pests and Diseases by Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2397-2402.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2022)08-2397-06 OR https://www.gpxygpfx.com/EN/Y2022/V42/I08/2397

[1] YUE Xue-jun, LING Kang-jie, WANG Lin-hui, et al(岳学军，凌康杰，王林惠，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2019, 50(3): 186.
[2] Gu Q, Sheng L, Zhang T H, et al. Computers and Electronics in Agriculture, 2019, 167: 105066.
[3] Zhou X, Sun J, Tian Y, et al. International Journal of Remote Sensing, 2020, 41(6): 2263.
[4] GUO Wei, ZHU Yao-hui, WANG Hui-fang, et al(郭伟，朱耀辉，王慧芳，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2019, 50(9): 162.
[5] De Morsier F, Borgeaud M, Gas V, et al. IEEE Transactions on Geoscience & Remote Sensing, 2016, 54(6): 3410.
[6] Wang J, Wu W, Wang T, et al. Spectroscopy Letters, 2018, 51(9): 485.
[7] LAN Yu-bin, ZHU Zi-hao, DENG Xiao-ling, et al(兰玉彬，朱梓豪，邓小玲，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2019, 35(3): 92.
[8] DENG Xiao-ling, ZENG Guo-liang, ZHU Zi-hao, et al(邓小玲，曾国亮，朱梓豪，等). Journal of South China Agricultural University(华南农业大学学报), 2020, 41(6): 100.
[9] Abdulridha J, Batuman O, Ampatzidis Y. Remote Sensing, 2019, 11(11): 1373.
[10] LIU Yan-de, XIAO Huai-chun, SUN Xu-dong, et al(刘燕德，肖怀春，孙旭东，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2016, 47(11): 231.
[11] SUN Xu-dong, LIU Yan-de, XIAO Huai-chun, et al(孙旭东，刘燕德，肖怀春，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(2): 551.
[12] Li H D, Liang Y Z, Xu Q S, et al. Analytica Chimca Acta, 2009, 648: 77.
[13] Tian X, Fan S X, Huang W Q, et al. Postharvest Biology and Technology, 2020, 161: 111071.
[14] DU Pei-jun, XIA Jun-shi, XUE Chao-hui, et al(杜培军，夏俊士，薛朝辉，等). Journal of Remote Sensing(遥感学报), 2016, 20(2): 236.
[15] Chen Tianqi, Guestrin C. XGBoost: A Scalable Tree Bossting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, 785.