Raspberry plant stress detection using hyperspectral imaging

被引:4
|
作者
Williams, Dominic [1 ]
Karley, Alison [1 ]
Britten, Avril [2 ]
McCallum, Susan [1 ]
Graham, Julie [1 ]
机构
[1] James Hutton Inst, Dundee, Scotland
[2] James Hutton Ltd, Dundee, Scotland
基金
“创新英国”项目;
关键词
CHLOROPHYLL CONTENT; SPECTRAL INDEX; REFLECTANCE; LEAF; FLUORESCENCE; VEGETATION; TRAITS; CANOPY;
D O I
10.1002/pld3.490
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Monitoring plant responses to stress is an ongoing challenge for crop breeders, growers, and agronomists. The measurement of below-ground stress is particularly challenging as plants do not always show visible signs of stress in the above-ground organs, particularly at early stages. Hyperspectral imaging is a technique that could be used to overcome this challenge if associations between plant spectral data and specific stresses can be determined. In this study, three genotypes of red raspberry plants grown under controlled conditions in a glasshouse were subjected to below-ground biotic stresses (root pathogen Phytophthora rubi and root herbivore Otiorhynchus sulcatus) or abiotic stress (soil water availability) and regularly imaged using hyperspectral cameras over this period. Significant differences were observed in plant biophysical traits (canopy height and leaf dry mass) and canopy reflectance spectrum between the three genotypes and the imposed stress treatments. The ratio of reflectance at 469 and 523 nm showed a significant genotype-by-treatment interaction driven by differential genotypic responses to the P. rubi treatment. This indicates that spectral imaging can be used to identify variable plant stress responses in raspberry plants.
引用
收藏
页数:14
相关论文
共 50 条
  • [41] Detection of Lettuce Discoloration Using Hyperspectral Reflectance Imaging
    Mo, Changyeun
    Kim, Giyoung
    Lim, Jongguk
    Kim, Moon S.
    Cho, Hyunjeong
    Cho, Byoung-Kwan
    SENSORS, 2015, 15 (11) : 29511 - 29534
  • [42] Object Detection in Rural Areas using Hyperspectral Imaging
    Ozturk, Safak
    Esin, Yunus Emre
    Artan, Yusuf
    IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING XXI, 2015, 9643
  • [43] Damage Detection in Composite Materials Using Hyperspectral Imaging
    Dlugosz, Jan
    Dao, Phong Ba
    Staszewski, Wieslaw J.
    Uhl, Tadeusz
    EUROPEAN WORKSHOP ON STRUCTURAL HEALTH MONITORING (EWSHM 2022), VOL 2, 2023, : 463 - 473
  • [44] Fraud detection in the fishing sector using hyperspectral imaging
    Esplandiu, Paula Luri
    Marin-Mendez, Juan-Jesus
    Alonso-Santamaria, Miriam
    Remirez-Moreno, Berta
    Saiz-Abajo, Maria-Jose
    JOURNAL OF NEAR INFRARED SPECTROSCOPY, 2024, 32 (03) : 69 - 80
  • [45] Detection and identification of plastics using SWIR hyperspectral imaging
    Mehrubeoglu, Mehrube
    Van Sickle, Austin
    Turner, Jeffrey
    IMAGING SPECTROMETRY XXIV: APPLICATIONS, SENSORS, AND PROCESSING, 2020, 11504
  • [46] Multivariate Hyperspectral Raman Imaging Using Compressive Detection
    Davis, Brandon M.
    Hemphill, Amanda J.
    Maltas, Derya Cebeci
    Zipper, Michael A.
    Wang, Ping
    Ben-Amotz, Dor
    ANALYTICAL CHEMISTRY, 2011, 83 (13) : 5086 - 5092
  • [47] Wind turbine ice detection using hyperspectral imaging
    Rizk, Patrick
    Younes, Rafic
    Ilinca, Adrian
    Khoder, Jihan
    REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT, 2022, 26
  • [48] Detection of hypercholesterolemia using hyperspectral imaging of human skin
    Milanic, Matija
    Bjorgan, Asgeir
    Larsson, Marcus
    Stromberg, Tomas
    Randeberga, Lise Lyngsnes
    CLINICAL AND BIOMEDICAL SPECTROSCOPY AND IMAGING IV, 2015, 9537
  • [49] Detection of Camouflaged Targets using Hyperspectral Imaging Technology
    Yang Jia
    Hua Wenshen
    Ma Zuohong
    Zhang Yue
    INTERNATIONAL SYMPOSIUM ON PHOTOELECTRONIC DETECTION AND IMAGING 2013: IMAGING SPECTROMETER TECHNOLOGIES AND APPLICATIONS, 2013, 8910
  • [50] Detection of acid rain stress effect on plant using hyperspectral data in Three Gorges region, China
    Song Xiaodong
    Jiang Hong
    Yu Shuquan
    Zhou Guomo
    CHINESE GEOGRAPHICAL SCIENCE, 2008, 18 (03) : 249 - 254