|
|
|
|
|
|
Early Nondestructive Detection of Wheat Stripe Rust Using Infrared Thermal Imaging |
YAO Zhi-feng1,2, HE Dong-jian1,2*, LEI Yu1,2 |
1. College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
2. Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rual Affairs, Yangling 712100, China |
|
|
Abstract Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici, is one of the most destructive diseases causing severe decreases in wheat yield. Early detection of wheat stripe rust before symptom appearance is of great significance for developing effective control strategies and taking timely management measures to ensure high and stable yield of wheat. The objective of this study is to investigate the possibility of thermal infrared imaging technology used for early detection of wheat stripe rust by assessing the temperature changes of normal and infected wheat leaves in a pot experiment under controlled conditions. Four pots of wheat plants were subjected to artificial inoculation with Puccinia striiformis once a day, lasting for 16 days, in an artificial climate chamber. Meanwhile, healthy wheat plants were chosen as the normal control. Thermal infrared images and data on leaf temperature of all the normal and infected wheat leaves were collected 16 days after inoculation (dpi) by using an infrared thermography. The results revealed that with the increasing of days after inoculation, the divergence in the average temperature and maximum temperature difference (MTD) between infected and healthy wheat leaves gradually increases. The infected wheat leaves can be distinguished from healthy ones 6 days after inoculation using infrared thermal imaging, that is, at least 4 days before visible symptoms appearance. At 16 dpi, the average temperature of the inoculated wheat leaves was 2.5 ℃ lower than that of healthy ones, and the MTD of the inoculated leaves was 2.28 ℃ larger than that of healthy ones. Fungal development was also assessed microscopically. It was found through microscopic observation and analysis that stripe rust (Puccinia striiformis) infection caused changes in the integrity of the epidermal cells, the structure of chloroplasts, as well as stomatal conductance and leaf transpiration rate involved in inducing defense. A decrease in leaf surface temperature after the infection was observed as a thermal signature of early infection of disease after successful germination, penetration and reproduction of urediniospores. Thus, thermal infrared imaging has great potential for early detection of wheat stripe rust, with noninvasive monitoring and direct visualization characteristics.
|
Received: 2017-10-17
Accepted: 2018-03-22
|
|
Corresponding Authors:
HE Dong-jian
E-mail: hdj168@nwsuaf.edu.cn
|
|
[1] Chen W Q,Wellings C,Chen X M,et al. Molecular Plant Pathology,2014, 15(5): 433.
[2] Zeng Q D,Han D J,Wang Q L,et al. Euphytica,2014,196(2): 271.
[3] Chemura A,Mutanga O,Dube T. Precision Agriculture,2017,18(5): 859.
[4] Fanelli E,Cotroneo A,Carisio L,et al. European Journal of Plant Pathology,2017,149(2): 467.
[5] Li Y R,Shang H S. Acta Tritical Crops,2004,21.
[6] Xu X L,Jiang H Y,Hang Y L. Transactions of the Chinese Society of Agricultural Engineering,2012,28(5): 145.
[7] Vadivambal R,Jayas D S. Food and Bioprocess Technology,2011,4(2): 186.
[8] I F Garcia-Tejero,Costa J M,Egipto R,et al. Agricultural Water Management,2016,176: 80.
[9] Martynenko A,Shotton K,Astatkie T,et al. SpringerPlus,2016,5(1): 1393.
[10] Rispail N,Rubiales D. Sensors,2015,15(2): 3988.
[11] Awad Y M,Abdullah A A,Bayoumi T Y,et al. Intelligent Systems’,2014,755.
[12] Kim J,Kweon S-G,Park J,et al. The Plant Pathology Journal,2016,32(6): 563.
[13] Masri A A,Hau B,Dehne H W,et al. European Journal of Plant Pathology,2016,147(4): 855.
[14] Li X L,Wang K,Ma Z H,et al. Transactions of the Chinese Society of Agricultural Engineering,2014,30(18): 183.
[15] Wang Z Y,Zhao J,Chen X M,et al. Plant Disease,2016,100(1): 131.
[16] Faye E,Dangles O,Pincebourde S. Journal of Thermal Biology,2016,56: 1.
[17] Lindenthal M,Steiner U,Dehne H W,et al. Phytopathology,2005,95(3): 233.
[18] Oerke E C,Frhling P,Steiner U. Precision Agriculture,2011,12(5): 699. |
[1] |
ZHANG Hong-tao, ZHAO Xin-tao, TAN Lian, WANG Long-jie. Research and Development of Microscopic Hyperspectral Imaging in
Biological Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2348-2353. |
[2] |
DUAN Wei-na1, 2, JING Xia1*, LIU Liang-yun2, ZHANG Teng1, ZHANG Li-hua3. Monitoring of Wheat Stripe Rust Based on Integration of SIF and Reflectance Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 859-865. |
[3] |
MU Bing-yu1, ZHANG Shu-juan1, LI Ze-zhen2, WANG Kai1, LI Zi-hui1, XUE Jian-xin1*. Early Detection of Cauliflower Gray Mold Based on Near-Infrared Spectrum Feature Extraction[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2543-2548. |
[4] |
LUO Lin-lin1, 2, 3, NIU Jing-jing3, MO Bei-xin1, 2, LIN Dan-ying3, LIU Lin1, 2*. Advances in the Application of Förster Resonance Energy Transfer and Fluorescence Lifetime Imaging Microscopy (FRET-FLIM) Technique in Life Science Research[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(04): 1023-1031. |
[5] |
KANG Li1, 2, YUAN Jian-qing3, GAO Rui1, KONG Qing-ming1, JIA Yin-jiang1, SU Zhong-bin1*. Early Detection and Identification of Rice Blast Based on Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(03): 898-902. |
[6] |
MA Xiao-dan1*, LIU Meng1, GUAN Hai-ou1, WEN Feng-rui1, LIU Gang2. Recognition Method for Crop Canopies Based on Thermal Infrared Image Processing Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(01): 216-222. |
[7] |
LU Wei1, HAN Zhao1, JIAN Xing-liang1, Zhou Ji2, JIANG Dong3, DING Yan-feng3. Maize Root Phenotypic Detection Based on Thermal Imaging and Root Gap Repair Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(09): 2845-2850. |
[8] |
SUN Qian1, 2, HUANG Rui-sheng1*, LEI Zhen1, YANG Yi-cheng1, WANG Xu-you1, LI Li-qun2. Study on Coaxial Synergistic Extraction Method of Laser Welding Penetration Characteristic Signal[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(03): 679-683. |
[9] |
HE Ming-xia1, TIAN Tian2, LIU Li-yuan*, BU Shao-chong, DONG Li-jie, ZHANG Xin-xin, ZHANG Hong-zhen. Synchrotron Radiation Infrared Microscopy Analysis of Mouse Trabecular Meshwork Cells and Myofibroblasts[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(11): 3346-3351. |
[10] |
ZHAO Ye1,3, JING Xia1*, HUANG Wen-jiang2, DONG Ying-ying2, LI Cun-jun3. Comparison of Sun-Induced Chlorophyll Fluorescence and Reflectance Data on Estimating Severity of Wheat Stripe Rust[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(09): 2739-2745. |
[11] |
CHEN Xin-xin1, 3, 4, LIU Zi-yi1, 3, 4, Lü Mei-qiao2*, ZHANG Chu1, 3, 4, YAO Jie-ni1, 2, HE Yong1, 3, 4*. Diagnosis and Monitoring of Sclerotinia Stem Rot of Oilseed Rape Using Thermal Infrared Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(03): 730-737. |
[12] |
HUANG Zu-fang, GE Xiao-song, LI Yong-zeng, CHEN Guan-nan, FENG Shang-yuan, LIN Ju-qiang, LEI Jin-ping. Diagnosis of Human Thyroid Diseases Based on Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(11): 3471-3474. |
[13] |
LIU Guo-yan1, GAO Kun2, LIU Xue-feng3, NI Guo-qiang2. Analysis on Near Field Spatial Spectra Scattering Modeling of Metallic Nanoparticle and Microscopic Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(09): 2662-2666. |
[14] |
WANG Fei1, WU De-jun1, ZHAI Guo-feng2, ZANG Li-peng3 . Diagnosing Low Health and Wood Borer Attacked Trees of Chinese Arborvitae by Using Thermography[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(12): 3410-3415. |
[15] |
LI Xiao-long1, QIN Feng1, ZHAO Long-lian2, LI Jun-hui2, MA Zhan-hong1, WANG Hai-guang1* . Identification and Classification of Disease Severity of Wheat Stripe Rust Using Near Infrared Spectroscopy Technology [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(02): 367-371. |
|
|
|
|