辣椒早疫病潜育期的光谱特性判别方法

doi:10.3964/j.issn.1000-0593(2024)10-2923-09

Abstract
References
Related Citation (15)

Download: PDF (18845 KB)
Export: BibTeX | EndNote (RIS)

Abstract Early blight of chili peppers is a common biological disaster that affects the safe production of chili peppers. It is characterized by suddenness and susceptibility and can easily cause significant economic losses. During the growth process of chili peppers, scientific monitoring and early warning of disease infestation during the incubation period is an important prerequisite for ensuring the healthy growth of crops. This paper establishes the spectral characteristics discriminative model of crop disease incubation period using the hyperspectral image with the working band of 400~1 000 nm and the spectral similarity measurement method. Continuous and dynamic monitoring of hyperspectral images of pepper leaves and healthy leaves inoculated with early blight pathogens at different infection stages using a hyperspectral imager.Extract the average spectrum of the region of interest from a series of hyperspectral images collected in the experiment and preprocess it using convolutional smoothing, multivariate scattering correction, and maximum minimum normalization method (SG-MSC-MMN). Then, two measures, spectral angle cosine correlation coefficient, and Chebyshev distance, are proposed as spectral characteristics evaluation parameters for the incubation period of early blight. Finally, principal component analysis (PCA) was used to verify the results of the spectral characteristics discriminative model of the incubation period to realize the visual distribution of the incubation period of samples.The experimental results show that it is feasible to use the spectral angle cosine correlation coefficient and Chebyshev distance as the spectral characteristic evaluation parameters of the incubation period of early blight of pepper and establish the corresponding discriminative model, respectively, and the earliest identifiable time of incubation period of early blight of pepper obtained from these two discriminative models is 24 hours after inoculation.According to the PCA drawing, the spatial distribution of health vaccination samples during inoculation for 24 hours was verified by the two spectral characteristics-based discriminative models for the incubation period proposed in this paper.The discriminative model of the incubation period of early blight of pepper established in this paper can be extended to monitor and identify the incubation period of other crop diseases and provide theoretical reference and method reference for scientific control of the incubation period of crop diseases.

Key words：Spectral characteristic; Spectral angle cosine correlation coefficient; Chebyshev distance; Early blight of chili pepper; The earlist identification time of incubation period

Received: 2023-05-15 Accepted: 2024-03-17

ZTFLH:

TP391.4

Corresponding Authors: ZHANG Yan E-mail: Eileen_zy001@sohu.com

Cite this article:

SHEN Meng-jiao,BAO Hao,ZHANG Yan. Spectral Characteristics Identification Method for the Incubation Period of Chili Early Blight Disease[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(10): 2923-2931.

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2024)10-2923-09 OR https://www.gpxygpfx.com/EN/Y2024/V44/I10/2923

[1] SHEN Meng-jiao, HOU Hai-min, ZHAO Hong, et al(沈梦姣, 侯海敏, 赵宏，等). Jiangsu Agricultural Sciences(江苏农业科学) 2023, 51(9): 17.
[2] Wang Jinhui, Xiao Siyu, Zheng Lijia, et al. Phytopathology Research, 2022, 4(1): 29.
[3] Karadağ K, Tenekeci M E, Tasaltin R, et al. Sustainable Computing: Informatics and Systems, 2020, 28: 100299.
[4] Atas M, Yardimci Y, Temizel A. Computers and Electronics in Agriculture, 2012, 87: 129.
[5] FENG Lei, GAO Ji-xing, HE Yong, et al(冯雷，高吉兴，何勇，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报)，2013，44(9)：169.
[6] Yang C H. Engineering, 2020, 6: 528.
[7] ZHOU Chang-jian, SONG Jia, XIANG Wen-sheng(周长建, 宋佳, 向文胜). Journal of Plant Protection(植物保护学报), 2022, 49(1): 316.
[8] Chen Tingting, Zhang Jialei, Chen Yong, et al. Computers and Electronics in Agriculture, 2019, 156: 677.
[9] Hou Bingru, Hu Yaohua, Zhang Peng, et al. Agriculture, 2022, 12(7): 897.
[10] Gao Jianmeng, Ding Mingliang, Sun Qiuyu, et al. Remote Sensing, 2022, 14(11): 2551.
[11] ZHU Wen-jing, LI Lin, LI Mei-qing, et al(朱文静，李林，李美清，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(9): 2757.
[12] Chang Chein-I. IEEE Transactions on Information Theory, 2000, 46(5): 1927.
[13] ZHAO Chun-hui, TIAN Ming-hua, LI Jia-wei(赵春晖，田明华，李佳伟). Journal of Harbin Engineering University(哈尔滨工程大学学报), 2017, 38(8): 1179.
[14] GAO Dong-yang, LONG Hua-bao, WU Shuang-qing, et al(高冬阳, 龙华保, 吴双卿，等). Journal of Atmospheric and Environmental Optics(大气与环境光学学报), 2020, 15(5): 393.
[15] WEI Xiang-po, YU Xu-chu, FU Qiong-ying, et al(魏祥坡, 余旭初, 付琼莹，等). Geography and Geo-Information Science(地理与地理信息科学), 2016, 32(3): 29.
[16] FAN Yan-guo, LI Xiang-yu, ZHANG Lei, et al(樊彦国, 李翔宇, 张磊，等). Geography and Geo-Information Science(地理与地理信息科学), 2010, 26(2): 38.
[17] Kruse F A, Lefkoff A B, Boardman J W, et al. AIP Conference Proceedings, 1993, 283: 192.