سابِق|تال

Comparison between two types of fungal diseases in specific spectral responses and monitoring performance

  • role： First author第一作者
  • Affiliation：

    National Engineering and Technology Center for Information Agriculture, MOE Engineering Research Center of Smart Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing210095, China

  • Email：2021201095@stu.njau.edu.cn
  • Introduction：薛博文，研究方向为作物病害遥感监测。E-mail： 2021201095@stu.njau.edu.cn
XUE Bowen,  
  • Affiliation：

    National Engineering and Technology Center for Information Agriculture, MOE Engineering Research Center of Smart Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing210095, China

KONG Yuanyuan,  
  • Affiliation：

    National Engineering and Technology Center for Information Agriculture, MOE Engineering Research Center of Smart Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing210095, China

TIAN Long,  
  • Affiliation：

    National Engineering and Technology Center for Information Agriculture, MOE Engineering Research Center of Smart Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing210095, China

WANG Xue,  
  • Affiliation：

    National Engineering and Technology Center for Information Agriculture, MOE Engineering Research Center of Smart Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing210095, China

YAO Xia,  
  • Affiliation：

    National Engineering and Technology Center for Information Agriculture, MOE Engineering Research Center of Smart Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing210095, China

ZHU Yan,  
  • Affiliation：

    National Engineering and Technology Center for Information Agriculture, MOE Engineering Research Center of Smart Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing210095, China

CAO Weixing,  
  • role： Corresponding author通信作者
  • Affiliation：

    National Engineering and Technology Center for Information Agriculture, MOE Engineering Research Center of Smart Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing210095, China

  • Email：tcheng@njau.edu.cn
  • Introduction：程涛，研究方向为农情遥感监测。E-mail： tcheng@njau.edu.cn
CHENG Tao*

ملخص

Remote sensing monitoring of crop diseases plays a crucial role in food security in terms of the precision management of chemical fungicides and the efficient assessment of crop losses. Spectroscopic detection of disease infection has been investigated for numerous crop diseases individually. However, it remains unclear how biochemical and spectral variations differ in response to divergent diseases given the distinct symptoms caused by different pathogens. This study aimed to determine the pathological mechanism and specificity of the spectral responses of two types of fungal diseases by comparing their specific spectral signatures and disease monitoring performance. The biotrophic Wheat Powdery Mildew (WPM) and the semi-biotrophic Rice Leaf Blast (RLB) diseases were used as examples for the comparison. With the reflectance measurements of infected leaves and radiative transfer modeling, a comparative analysis for these two diseases was conducted in terms of spectral responses, leaf biochemical and structural parameters. Additionally, we assessed the specificity of various disease-related spectral features, which were proposed in previous studies for the monitoring of WPM or RLB, by accuracy comparison in the detection of diseased leaves and the estimation of Leaf Lesion Proportion (LLP). The results showed significant differences in the intensity of spectral responses to the two diseases despite the similarity observed in the general trend in spectral variations. In addition, distinct variations appeared in the spectral shape at the green peak and near-infrared plateau between WPM and RLB. Moreover, the pigment variations in response to two infections were generally similar, whereas the response was more pronounced for RLB. Notably, the leaf water content and structural parameter displayed significant changes only in relation to the severity of RLB. In disease detection, the spectral features developed for WPM or RLB generated higher accuracy in detection of the target disease than the other disease. Wavelet features of WF3820 and WF5866 displayed the highest accuracy and specificity for WPM and RLB, respectively. Regarding the severity quantification, most spectral features exhibited higher sensitivity to the LLP of RLB than to that of WPM. Specifically, a variat of rice blast index (RIBIred) and the Photochemical Reflectance Index (PRI) demonstrated the highest accuracy and specificity in the LLP estimation of WPM and RLB, respectively. Among the WPM- or RLB-related spectral features, RIBIred showed the optimal monitoring performance and specificity in both disease detection and severity estimation (Overall accuracy=0.74, R2=0.58). Our findings provide solid evidence and new insights into disease-specific spectroscopic monitoring by associating spectral responses with pathogenesis of two types of fungal diseases. This study offers significant contributions to the understanding of disease monitoring mechanisms and the identification of multiple diseases with hyperspectral remote sensing.

مفهوم

remote sensing;Disease detection;severity quantification;PROSPECT;biochemical parameters;leaf structure;spectral index;continuous wavelet transform

References

  1. 1.
    Abd El-Ghany N M, Abd El-Aziz S E and Marei S S. 2020. A review: application of remote sensing as a promising strategy for insect pests and diseases management. Environmental Science and Pollution Research, 27(27): 33503-33515.
  2. 2.
    Bapela T, Shimelis H, Terefe T, Bourras S, Sánchez-Martín J, Douchkov D, Desiderio F and Tsilo T J. 2023. Breeding Wheat for Powdery Mildew Resistance: Genetic Resources and Methodologies—A Review: 4. Agronomy, 13(4): 1173.
  3. 3.
    Berger K, Machwitz M, Kycko M, Kefauver S C, Van Wittenberghe S, Gerhards M, Verrelst J, Atzberger C, van der Tol C, Damm A, Rascher U, Herrmann I, Paz V S, Fahrner S, Pieruschka R, Prikaziuk E, Buchaillot Ma L, Halabuk A, Celesti M, Koren G, Gormus E T, Rossini M, Foerster M, Siegmann B, Abdelbaki A, Tagliabue G, Hank T, Darvishzadeh R, Aasen H, Garcia M, Pôças I, Bandopadhyay S, Sulis M, Tomelleri E, Rozenstein O, Filchev L, Stancile G and Schlerf M. 2022. Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review. Remote Sensing of Environment, 280: 113198.
  4. 4.
    Boddy L. 2016. Chapter 8 - Pathogens of Autotrophs//The Fungi (Third Edition). Boston: Academic Press:245-292.
  5. 5.
    Calderón R, Navas-Cortés J A, Lucena C and Zarco-Tejada P J. 2013. High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sensing of Environment, 139: 231-245.
  6. 6.
    Cheng T, Rivard B, Sánchez-Azofeifa G A, Feng J and Calvo-Polanco M. 2010. Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation. Remote Sensing of Environment, 114(4): 899-910.
  7. 7.
    Feng W, Wang X Y, Song X, He L, Wang Y H and Guo T C. 2015. Estimation model of wheat powdery mildew severity based on leaves hyperspectral characteristics. Journal of Triticeae Crops, 35(1): 129-137
  8. 8.
    Féret J-B, Gitelson A A, Noble S D and Jacquemoud S. 2017. PROSPECT-D: Towards modeling leaf optical properties through a complete lifecycle. Remote Sensing of Environment, 193: 204-215.
  9. 9.
    Gamon J A, Peñuelas J and Field C B. 1992. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41(1): 35-44.
  10. 10.
    Huang W, Guan Q, Luo J, Zhang J, Zhao J, Liang D, Huang L and Zhang D. 2014. New Optimized Spectral Indices for Identifying and Monitoring Winter Wheat Diseases. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 2516-2524.
  11. 11.
    Huang W J, Shi Y, Dong Y Y, Ye H C, Wu M Q, Cui B and Liu L Y. 2019. Progress and prospects of crop diseases and pests monitoring by remote sensing. Smart Agriculture, 1(04): 1-11
  12. 12.
    Huo L, Persson H J and Lindberg E. 2021. Early detection of forest stress from European spruce bark beetle attack, and a new vegetation index: Normalized distance red & SWIR (NDRS). Remote Sensing of Environment, 255: 112240.
  13. 13.
    Jeon H S, Jang E, Kim J, Kim S H, Lee M-H, Nam M H, Tobimatsu Y and Park O K. 2023. Pathogen-induced autophagy regulates monolignol transport and lignin formation in plant immunity. Autophagy, 19(2): 597-615.
  14. 14.
    Jiang X, Zhen J, Miao J, Zhao D, Shen Z, Jiang J, Gao C, Wu G and Wang J. 2022. Newly-developed three-band hyperspectral vegetation index for estimating leaf relative chlorophyll content of mangrove under different severities of pest and disease. Ecological Indicators, 140: 108978.
  15. 15.
    Jiang Z, Dong Z, Jiang W and Yang Y. 2021. Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning. Computers and Electronics in Agriculture, 186: 106184.
  16. 16.
    Laurent B, Moinard M, Spataro C, Ponts N, Barreau C and Foulongne-Oriol M. 2017. Landscape of genomic diversity and host adaptation in Fusarium graminearum. BMC Genomics, 18(1): 203.
  17. 17.
    Li D, Cheng T, Jia M, Zhou K, Lu N, Yao X, Tian Y, Zhu Y and Cao W. 2018. PROCWT: Coupling PROSPECT with continuous wavelet transform to improve the retrieval of foliar chemistry from leaf bidirectional reflectance spectra. Remote Sensing of Environment, 206: 1-14.
  18. 18.
    Mahlein A-K. 2016. Plant Disease Detection by Imaging Sensors - Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping. Plant Disease, 100(2): 241-251.
  19. 19.
    Mahlein A-K, Kuska M T, Behmann J, Polder G and Walter A. 2018. Hyperspectral Sensors and Imaging Technologies in Phytopathology: State of the Art. Annual Review of Phytopathology, 56(1): 535-558.
  20. 20.
    Mahlein A-K, Kuska M T, Thomas S, Wahabzada M, Behmann J, Rascher U and Kersting K. 2019. Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed!Current Opinion in Plant Biology, 50: 156-162.
  21. 21.
    Mahlein A-K, Rumpf T, Welke P, Dehne H-W, Plümer L, Steiner U and Oerke E-C. 2013. Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment, 128: 21-30.
  22. 22.
    Merzlyak M N, Chivkunova O B, Solovchenko A E and Naqvi K R. 2008. Light absorption by anthocyanins in juvenile, stressed, and senescing leaves. Journal of Experimental Botany, 59(14): 3903-3911.
  23. 23.
    Mustafa G, Zheng H, Khan I H, Tian L, Jia H, Li G, Cheng T, Tian Y, Cao W, Zhu Y and Yao X. 2022. Hyperspectral Reflectance Proxies to Diagnose In-Field Fusarium Head Blight in Wheat with Machine Learning: 12. Remote Sensing, 14(12): 2784.
  24. 24.
    Pacheco-Labrador J, El-Madany T S, van der Tol C, Martin M P, Gonzalez-Cascon R, Perez-Priego O, Guan J, Moreno G, Carrara A, Reichstein M and Migliavacca M. 2021. senSCOPE: Modeling mixed canopies combining green and brown senesced leaves. Evaluation in a Mediterranean Grassland. Remote Sensing of Environment, 257: 112352.
  25. 25.
    Peñuelas J, Filella I, Lloret P, MunOz F and Vilajeliu M. 1995. Reflectance assessment of mite effects on apple trees. International Journal of Remote Sensing, 16(14): 2727-2733.
  26. 26.
    Ren Y, Huang W, Ye H, Zhou X, Ma H, Dong Y, Shi Y, Geng Y, Huang Y, Jiao Q and Xie Q. 2021. Quantitative identification of yellow rust in winter wheat with a new spectral index: Development and validation using simulated and experimental data. International Journal of Applied Earth Observation and Geoinformation, 102: 102384.
  27. 27.
    Thomas S, Wahabzada M, Kuska M T, Rascher U and Mahlein A-K. 2017. Observation of plant-pathogen interaction by simultaneous hyperspectral imaging reflection and transmission measurements. Functional Plant Biology, 44(1): 23.
  28. 28.
    Tian L, Wang Z, Xue B, Li D, Zheng H, Yao X, Zhu Y, Cao W and Cheng T. 2023. A disease-specific spectral index tracks Magnaporthe oryzae infection in paddy rice from ground to space. Remote Sensing of Environment, 285: 113384.
  29. 29.
    Tian L, Xue B, Wang Z, Li D, Yao X, Cao Q, Zhu Y, Cao W and Cheng T. 2021. Spectroscopic detection of rice leaf blast infection from asymptomatic to mild stages with integrated machine learning and feature selection. Remote Sensing of Environment, 257: 112350.
  30. 30.
    Wang C, Wang X, Jin Z, Müller C, Pugh T A M, Chen A, Wang T, Huang L, Zhang Y, Li L X Z and Piao S. 2022. Occurrence of crop pests and diseases has largely increased in China since 1970: 1. Nature Food, 3(1): 57-65.
  31. 31.
    Wen J, Wu X, Xiao Q, Liu Q, Ma M, Zheng X, Qu Y, Jin R, You D, Tang Y, Lin X, Yu W, Gong B, Yang J and Han Y. 2023. Full-band, multi-angle, multi-scale, and temporal dynamic field spectral measurements in China. Scientific Data, 10(1): 353.
  32. 32.
    Xia D, Zhou H, Wang Y, Li P, Fu P, Wu B and He Y. 2021. How rice organs are colored: The genetic basis of anthocyanin biosynthesis in rice. Crop Journal, 9(3): 598-608.
  33. 33.
    Xue B, Tian L, Wang Z, Wang X, Yao X, Zhu Y, Cao W and Cheng T. 2023. Quantification of rice spikelet rot disease severity at organ scale with proximal imaging spectroscopy. Precision Agriculture.
  34. 34.
    Yang C-M. 2010. Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectance. Precision Agriculture, 11(1): 61-81.
  35. 35.
    Zarco-Tejada P J, Camino C, Beck P S A, Calderon R, Hornero A, Hernández-Clemente R, Kattenborn T, Montes-Borrego M, Susca L, Morelli M, Gonzalez-Dugo V, North P R J, Landa B B, Boscia D, Saponari M and Navas-Cortes J A. 2018. Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nature Plants, 4(7): 432-439.
  36. 36.
    Zarco-Tejada P J, Poblete T, Camino C, Gonzalez-Dugo V, Calderon R, Hornero A, Hernandez-Clemente R, Román-Écija M, Velasco-Amo M P, Landa B B, Beck P S A, Saponari M, Boscia D and Navas-Cortes J A. 2021. Divergent abiotic spectral pathways unravel pathogen stress signals across species. Nature Communications, 12(1): 6088.
  37. 37.
    Zhang J, Huang Y, Pu R, Gonzalez-Moreno P, Yuan L, Wu K and Huang W. 2019. Monitoring plant diseases and pests through remote sensing technology: A review. Computers and Electronics in Agriculture, 165: 104943.
  38. 38.
    Zhang D, Wang Q, Lin F, Yin X, Gu C and Qiao H. 2020. Development and Evaluation of a New Spectral Disease Index to Detect Wheat Fusarium Head Blight Using Hyperspectral Imaging. Sensors, 20(8): 2260.
  39. 39.
    Zhang J, Gao S Y, Du Z L, An Y, Zhang C C and Yao Y P. 2022. Contamination status and control of mycotoxins in grain and oil crops. Journal of Agro-Environment Science, 41(12): 2680-2687.
  40. 40.
    Zhang J, Wang N, Yuan L, Chen F and Wu K. 2017. Discrimination of winter wheat disease and insect stresses using continuous wavelet features extracted from foliar spectral measurements. Biosystems Engineering, 162: 20-29.
  41. 41.
    Zhang N, Yang G J, Zhao C J, Zhang J C, Yang X D, Pan Y C, Huang W J, Xu B, Li M, Zhu X C and Li Z H. 2021. Progress and prospects of hyperspectral remote sensing technology for crop diseases and pests. National Remote Sensing Bulletin, 25(1): 403-422

قراءة النص الكامل

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website