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Open Access

Monitoring Wheat Powdery Mildew (Blumeria graminis f. sp. tritici) Using Multisource and Multitemporal Satellite Images and Support Vector Machine Classifier

Jinling Zhao1, Shizhou Du2( ), Linsheng Huang1
National Engineering Research Center for Analysis and Application of Agro-Ecological Big Data, Anhui University, Hefei 230601, China
Institute of Crops, Academy of Agricultural Sciences, Hefei 230031, China
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Abstract

Since powdery mildew (Blumeria graminis f. sp. tritici) mainly infects the foliar of wheat, satellite remote sensing technology can be used to monitor and assess it on a large scale. In this study, multisource and multitemporal satellite images were used to monitor the disease and improve the classification accuracy. Specifically, four Landsat-8 thermal infrared sensor (TIRS) and twenty MODerate-resolution imaging spectroradiometer (MODIS) temperature product (MOD11A1) were used to retrieve the land surface temperature (LST), and four Chinese Gaofen-1 (GF-1) wide field of view (WFV) images was used to identify the wheat-growing areas and calculate the vegetation indices (VIs). ReliefF algorithm was first used to optimally select the vegetation index (VIs) sensitive to wheat powdery mildew, spatial-temporal fusion between Landsat-8 LST and MOD11A1 data was performed using the spatial and temporal adaptive reflectance fusion model (STARFM). The Z-score standardization method was then used to unify the VIs and LST data. Four monitoring models were then constructed through a single Landsat-8 LST, multitemporal Landsat-8 LSTs (SLST), cumulative MODIS LST (MLST) and the combination of cumulative Landsat-8 and MODIS LST (SMLST) using the Support Vector Machine (SVM) classifier, that were LST-SVM, SLST-SVM, MLST-SVM and SMLST-SVM. Four assessment indicators including user accuracy, producer accuracy, overall accuracy and Kappa coefficient were used to compare the four models. The results showed that, the proposed SMLST-SVM obtained the best identification accuracies. The overall accuracy and Kappa coefficient of the SMLST-SVM model had the highest values of 81.2% and 0.67, respectively, while they were respectively 76.8% and 0.59 for the SLST-SVM model. Consequently, multisource and multitemporal LSTs can considerably improve the differentiation accuracies of wheat powdery mildew.

Keywords

wheat powdery mildewGF-1MODISLandsat-8land surface temperaturesupport vector machine

References

[1]

ZHANG J, WANG N, YUAN L, et al. Discrimination of winter wheat disease and insect stresses using continuous wavelet features extracted from foliar spectral measurements[J]. Biosystems Engineering, 2017, 162: 20-29.
Crossref Google Scholar
[2]

FENG W, SHEN W Y, HE L, et al. Improved remote sensing detection of wheat powdery mildew using dual-green vegetation indices[J]. Precision Agriculture, 2016, 17(5): 608-627.
Crossref Google Scholar
[3]

GALLEGO F J, KUSSUL N, SKAKUN S, et al. Efficiency assessment of using satellite data for crop area estimation in Ukraine[J]. International Journal of Applied Earth Observation and Geoinformation, 2014, 29: 22-30.
Crossref Google Scholar
[4]

SETHY P K, BARPANDA N K, RATH A K, et al. Image processing techniques for diagnosing rice plant disease: A survey[J]. Procedia Computer Science, 2020, 167: 516-530.
Crossref Google Scholar
[5]

YANG C. Remote sensing and precision agriculture technologies for crop disease detection and management with a practical application example[J]. Engineering, 2020, 6(5): 528-532.
Crossref Google Scholar
[6]

ZHENG Q, YE H, HUANG W, et al. Integrating spectral information and meteorological data to monitor wheat yellow rust at a regional scale: A case study[J]. Remote Sensing, 2021, 13(2): ID 278.
Crossref Google Scholar
[7]

HUANG W J, LAMB D W, NIU Z, et al. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging[J]. Precision Agriculture, 2007, 8(4-5): 187-197.
Crossref Google Scholar
[8]

ZHANG J C, PU R L, WANG J H, et al. Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements[J]. Computers and Electronics in Agriculture, 2012, 85: 13-23.
Crossref Google Scholar
[9]

LUO J H, ZHAO C J, HUANG W J, et al. Discriminating wheat aphid damage degree using 2-dimensional feature space derived from Landsat 5 TM[J]. Sensor Letters, 2012, 10(1-2): 608-614.
Crossref Google Scholar
[10]

ZHENG Q, HUANG W, CUI X, et al. New spectral index for detecting wheat yellow rust using sentinel-2 multispectral imagery[J]. Sensors, 2018, 18(3): ID 868.
Crossref Google Scholar
[11]

HE L, QI S, DUAN J, et al. Monitoring of Wheat powdery mildew disease severity using multiangle hyperspectral remote sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(2): 979-990.
Crossref Google Scholar
[12]

ZHAO J, FANG Y, CHU G, et al. Identification of leaf-scale wheat powdery mildew (Blumeria graminis f. sp. Tritici) combining hyperspectral imaging and an SVM classifier[J]. Plants, 2020, 9(8): ID 936.
Crossref Google Scholar
[13]

KHAN I H, LIU H, LI W, et al. Early detection of powdery mildew disease and accurate quantification of its severity using hyperspectral images in wheat[J]. Remote Sensing, 2021, 13(18): ID 3612.
Crossref Google Scholar
[14]

ZHAO J, XU C, XU J, et al. Forecasting the wheat powdery mildew (Blumeria graminis f. Sp. tritici) using a remote sensing-based decision-tree classification at a provincial scale[J]. Australasian Plant Pathology, 2018, 47(1): 53-61.
Crossref Google Scholar
[15]

LIANG W, CARBERRY P, WANG G, et al. Quantifying the yield gap in wheat-maize cropping systems of the Hebei Plain, China[J]. Field Crops Research, 2011, 124(2): 180-185.
Crossref Google Scholar
[16]

CHEN H, ZHANG H, LI Y. Review on research of meteorological conditions and prediction methods of crop disease and insect pest[J]. Chinese Journal of Agrometeorology, 2007, 28(2): 212-216.
Google Scholar
[17]

YUAN L, PU R L, ZHANG J C, et al. Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale[J]. Precision Agriculture, 2016, 17(3): 332-348.
Crossref Google Scholar
[18]

LOVELAND T R, IRONS J R. Landsat 8: The plans, the reality, and the legacy[J]. Remote Sensing of Environment, 2016, 185: 1-6.
Crossref Google Scholar
[19]

WANG W, LIANG S, MEYERS T. Validating MODIS land surface temperature products using long-term nighttime ground measurements[J]. Remote Sensing of Environment, 2008, 112(3): 623-635.
Crossref Google Scholar
[20]

HUANG L, JIANG J, HUANG W, et al. Wheat yellow rust monitoring based on Sentinel-2 Image and BPNN model[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(17): 178-185.
Google Scholar
[21]

LIU R, ZAHNG S, JIA R. Application of feature selection method in building information extracting from high resolution remote sensing image[J]. Bulletin of Surveying and Mapping, 2018, (2): 126-130.
Google Scholar
[22]

BAO W, ZHAO J, HU G, et al. Identification of wheat leaf diseases and their severity based on elliptical-maximum margin criterion metric learning[J]. Sustainable Computing: Informatics and Systems, 2021, 30: ID 100526.
Crossref Google Scholar
[23]

QIN F, LIU D, SUN B, et al. Identification of alfalfa leaf diseases using image recognition technology[J]. PLoS One, 2016, 11(12): ID e0168274.
Crossref Google Scholar
[24]

ROBNIK-ŠIKONJA M, KONONENKO I. Theoretical and empirical analysis of ReliefF and RReliefF[J]. Machine Learning, 2003, 53(1): 23-69.
Crossref Google Scholar
[25]

JORDAN C F. Derivation of leaf‐area index from quality of light on the forest floor[J]. Ecology, 1969, 50(4): 663-666.
Crossref Google Scholar
[26]
ROUSE J W, HAAS R H, SCHELL J A, et al. Monitoring vegetation systems in the great plains with ERTS[C]// In Third ERTS Symposium Volume 1: Technical Presentations. Washington, DC, USA: NASA, 1973: 309-317.
[27]

GAMON J A, PENUELAS J, FIELD C B. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency[J]. Remote Sensing of Environment, 1992, 41: 35-44.
Crossref Google Scholar
[28]

HUETE A R. A soil-adjusted vegetation index (SAVI)[J]. Remote Sensing of Environment, 1988, 25(3): 295-309.
Crossref Google Scholar
[29]

LIU H, HUETE A. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise[J]. IEEE Transactions on Geoscience and Remote Sensing, 1995, 33 (2): 457-465.
Crossref Google Scholar
[30]

BROGE N H, LEBLANC E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density[J]. Remote Sensing of Environment, 2000, 76(2): 156-172.
Crossref Google Scholar
[31]

RICHARDSON A J, WIEGAND C L. Distinguishing vegetation from soil background information[J]. Photogrammetric Engineering and Remote Sensing, 1977, 43(12): 1541-1552.
Google Scholar
[32]

PENUELAS J, BARET F, FILELLA I. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance[J]. Photosynthetica, 1995, 31(2): 221-230.
Google Scholar
[33]

DU C, REN H, QIN Q, et al. A practical split-window algorithm for estimating land surface temperature from Landsat 8 data[J]. Remote Sensing, 2015, 7(1): 647-665.
Crossref Google Scholar
[34]

REN H, DU C, LIU R, et al. Atmospheric water vapor retrieval from Landsat 8 thermal infrared images[J]. Journal of Geophysical Research: Atmospheres, 2015, 120(5): 1723-1738.
Crossref Google Scholar
[35]

GAO F, MASEK J, SCHWALLER M, et al. On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(8): 2207-2218.
Crossref Google Scholar
[36]

OLATOMIWA L, MEKHILEF S, SHAMSHIRBAND S, et al. A support vector machine-firefly algorithm-based model for global solar radiation prediction[J]. Solar Energy, 2015, 115: 632-644.
Crossref Google Scholar
[37]

HUANG L, LIU W, HUANG W, et al. Remote sensing monitoring of winter wheat powdery mildew based on wavelet analysis and support vector machine[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(14): 188-195.
Google Scholar
[38]

MA H, HUANG W, JING Y. Wheat powdery mildew forecasting in filling stage based on remote sensing and meteorological data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(9): 165-172.
Google Scholar
[39]

XU H. Retrieval of the reflectance and land surface temperature of the newly-launched Landsat 8 satellite[J]. Chinese Journal of Geophysics-Chinese Edition, 2015, 58(3): 741-747.
Google Scholar
[40]

SHARMA A K, SHARMA R K, BABU K S, et al. Effect of planting options and irrigation schedules on development of powdery mildew and yield of wheat in the North Western plains of India[J]. Crop Protection, 2004, 23(3): 249-253.
Crossref Google Scholar
Smart Agriculture
Volume 4 Issue 1-4,
2022
Pages 17-28
DOI: 10.12133/j.smartag.SA202202009
Cite this article:
Zhao J, Du S, Huang L. Monitoring Wheat Powdery Mildew (Blumeria graminis f. sp. tritici) Using Multisource and Multitemporal Satellite Images and Support Vector Machine Classifier. Smart Agriculture, 2022, 4(1): 17-28. https://doi.org/10.12133/j.smartag.SA202202009

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Received: 20 August 2021
Published: 30 March 2022
© The Author(s) 2022.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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