Prospects of satellite remote sensing in cereal disease

Prospects of satellite remote sensing in cereal
disease monitoring and precision crop protection
for food security enhancement in Pakistan
Syed Jawad Ahmad Shah and Muhammad Ibrahim
Nuclear Institute for Food and Agricuture (NIFA), Tarnab,
Peshawar, Pakistan
[email protected], [email protected]
Among cereals, wheat is one of the most critically important staple foods in Pakistan where
most of the population rely heavily on its production for their livelihoods. Population of
Pakistan is expected to get doubled by 2050, making it 4th largest nation. To meet the needs
of the growing population, wheat productivity on sustainable bases is of paramount
importance for food security. Historically, a set of biotic stresses caused by airborne fungi
can seriously affect wheat production in Pakistan which included yellow rust, leaf rust and
stem rust. Historical presidents in our wheat dependent country indicate that a disease
outbreak could cost millions of dollars in attempted control and lost agricultural output.. Such
losses can be minimized by the application of modern outer space technologies such as
satellite remote sensing which is not practiced in the ongoing phytopathological research in
Pakistan. Prospects of satellite remote sensing in surveillance, monitoring and precision
wheat crop protection of these rust diseases are presented for food security enhancement in
Figure 1: Three wheat rusts a: yellow rust; b: leaf rust and c: stem rust
Wheat acreage on global scale is around 215 million
hectares, 44%(95 million hectares) is in Asia where it is
grown on 62 million ha in China, India and Pakistan as shown
in figure 2.
Increased wheat production for self sufficiency and food
security is of strategic importance in most Asian countries
where majority of the farmers are poor with small holdings
and involved in subsistence farming.The three rust diseases
of wheat have historically been the major biotic stress (Fig1)
responsible for destabilizing production in Asia and other
parts of the world.
Fig 2: Countries with 2 third wheat acreage in
Pakistan although with the 2nd highest wheat acreage
among the Southeast Asian countries has a national average
yield at around 2.5t⁄ ha..Wheat is cultivated on 22 million ha
in Pakistan and it occupies 70% of the Rabi and 37% of the
total cropped area. It is being consumed @ of 135 kg/year
with 72% total calories intake.
A worth of 6.4 billion US$ wheat produced in the country and
diseases are one of the main causes for reducing its
production in the country where one percent loss in
production accounts for a loss of 61 milliion US$. Leaf rust
can attack 80% of the wheat acreage in Pakistan (Fig 3).
Fig 3; Lead rust prone areas in Pakistan
In Pakistan, wheat is cultivated on more than eight million
hectares and 70% of it is prone to yellow rust. Infestation is
severe in the foothills in the north, but is also present in the
central region and western upland areas (Fig 4). Most of the
Khyber Pakhtunkhwa is vulnerable to the disease and the
region is the gate way of new races of the pathogen entering
from neighboring countries.
Stem rust has been under control since the semi dwarf
spring wheats of the green revolution, which were stem rust
resistant and occupied most of the area since 1960s. It
reemerged in 2005 in Kaghan and is also reported from
Punjab and prevelent in most of Sindh Provience (including
Jhudo, Umarkot, Khhipro, Tandojam, Kunri, Samaro, Tando
Muhammad Khan, Bulri Shah Karim, Khipro, NIA Tandojam.
Bulri Shah Karim, Rehmani Nagar, Jhudo, Shah Bander,
Thatta, Matli, Nasrpur, Kisana mori, Sakrand, Gharo and
Karach) of Pakistan (Fig 5).
Fig 4; Yellow rust prone areas in Pakistan
Fig 5; Stem rust prone areas in Pakistan
Rust epidemic history and losses
Yellow rust: Thirteen epidemics have been recorded
since 1948. Four major yellow rust epidemics were
recorded in 1978, 1997–98 and 2005 and caused
respective losses of US$244 million, $33 million and $100
million to the Pakistan economy.
Epidemic years in Pakistan
Leaf rust: The sever epidemic of 1948 and 1954 reduced
grain yield by 30-50% while in 1978, it caused estimated
national losses of 10% in yield with an economic value of
86 million US$.
Stem rust: Two epidemics were reported but losses were
not estimated. Most of the Pakistani wheat genotypes
were found susceptible to stem rust during 2005-09.
Susceptibility of Pakistani wheat genotypes
to stem rust
Wheat rust surveillance & monitoring methods
For effective control of wheat rusts, it is essential to carry out disease
surveillance and monitoring to obtain the information on the incidence of
the disease timely and accurately. Following three approaches are
generally used and being developed for wheat rust monitoring and crop
► Phenotypic rust assessments
► Biochemical and molecular detection
► Remote sensing technology
Monitoring of rust diseases in Pakistan is mainly done through field
surveys by human power, which is time-consuming, energy consuming
and error prone. The subjectivity of the monitoring results seriously affect
the accuracy of disease forecast.
Biochemical and molecular detection is focusing on very early stage of
pathogen detection.
Development and implementation of remote sensing technologies
have facilitated the direct detection of foliar diseases quickly,
conveniently, economically and accurately under field conditions.
Levels of wheat rust monitoring using remote sensing technologies
In recent years, significant progress is made in
remote sensing technologies for monitoring wheat
rust at following four levels
► Single Leaf scale (ground based)
► Canopy scale (ground based)
► Field crop scale (aerial)
► Countries/regional scale (satellite based)
Remote sensing data at single leaf, canopy and
field crop scale levels provide local and limited
experimental information.
While satellite based remote sensing can provide
a sufficient and inexpensive data base for rust
over large wheat regions or at spatial scale. It also
offers the advantage of continuously collected
data and availability of immediate or archived data
sets. Some examples of successful satellite and
other remote sensing techniques used for
detecting wheat rust and other crop diseases are
presented in Table 1.
Receiving station
Table 1: Satellite and other remote sensing techniques used
for detecting wheat rust and other crop diseases
Yellow rust
Huang et al., 2004
Yellow rust
SPOT5 image
Zhang et al., 2009
Yellow rust
Landsat TM images
Liu et al., 2009
Liu et al., 2009
Nagarajan et al.,2009
Yellow & leaf
Leaf rust
Earth Technology Satelite-1
Yellow rust
Satellite images (HJ-CCD)
Kanemasu et al.,
Zhang et al., 2011
Chen et al., 2007
Leaf rust
Streak mosaic
Landsat Thematic Mapper
Airborne and space borne
Landsat 5 Thematic Mapper
Mirik et al., 2011
Sheath blight
ADAR system 5500
Zhihao et al., 2003
Cyst nematode
Landsat 7
Nutter et al., 2002
leaf spot
leaf fall
Multi-date satellite data of IRS1C
Ranganath et al.,
Franke & Menz, 2007
Table 1: Continued
Yellow rust
Pushbroom Hyperspectral
Imager (PIH) sensor
Int. J. Agric. Biol., 2012, China
Yellow rust
ASD FieldSpec, 2002-03
yellow rust
In-field spectral images
Computers and Electronics in
Agriculture,2004, UK
Powdery mildew
Airborne hyperspectral imaging
2008 SPIE, Germany
FieldSpec TM
spectroradiometer and a
multispectral CDD camera.
Sens. & Instrumen. Food Qual.
Orange rust
EO-1 Hyperion imagery
Apan et al. (2004a), Australia
Suger beet
Powdery mildew
Airborne hyperspectral imaging
ROSIS, HyMap sensor systems
Proc. of SPIE Vol. 7472, 747228·
© 2009 SPIE
Pinus radiata
Needle blight
Hyperspectral imagery (CASI-2)
Phytopathology, 2003, Australia
Sugar beet
Root rot
QuickBird satellite image
Geoinformatics 2004, Germany
Early blight
ASD FieldSpec
Proceedings of SSC 2005, Austr.
Sheath blight
ADAR System 5500
Int J of Applied Earth
Observation & Geoinformation,
2005, USA
Space borne
Datta et al., 2006
Oil Palm
Stem rot
Space borne
EO-1 Hyperion imagery
Santoso et al., 2011
Apan et al., 2004
Hyperspectral satellite imagery
Blake et al., 2005
Establishment of wheat rust monitoring and warning
information system: A proposed collaborative program
Information to
& facilities
Early detection
& warning
R & D work on
integration of 3S
Food for thought
Remote sensing, GIS, and GPS technologies have the potential to revolutionize the
way in which farmers manage diseases in their crops, however, key conceptual and
quantitative links concerning the relationships between remote sensing data and
biological systems are often lacking. Where we stand in Pakistan?
Our ultimate goal is to develop methodologies to provide timely assessments
concerning the wheat and other crops health for regional as well as for individual
fields to facilitate the generation and delivery of timely, reliable, and cost effective
disease/crop management information. Needs research and development efforts in
To fully maximize the yield benefits that might be achieved by integrating 3S
technologies into disease management programs, it is first necessary to:
► Accurately differentiate vegetation types
► Recognize when wheat plants in fields are stressed
► Accurately determine what is causing plant stress
►Quantify the degree of plant stress within fields
► Quantify and relate remote sensing assessments for plant stress with ground
assessments for plant health.
► Develop crop stress thresholds for plant populations that alert the farmer
when available and mitigation measures should be developed to optimize the
farmer’s net return on investment
Wheat Rust Collaborating Institutions
► Cereal Disease Laboratory, St. Paul, Minnesota, USA
► Washington State University, USA
► International Maize and Wheat Improvement Center, Mexico.
► Institut National de la Recherche Agronomique (INRA), France.
► Plant Breeding Institute, University of Sydeny, Australia.
► National Crop Disease Research Program, NARC, Islamabad.
► National Wheat Improvement Program, NARC, Islamabad.
► Agricutural University, Peshawar.
► International Islamic University, Islamabad.
► Khyber Pakhtunkhwa Agricuture Research and Extension System.
Huang, M.Y., Huang, Y.D., Huang, W.J., Liu, L.Y., Wang, J.H., Wan, A.M.: The Physiological Changes of Winter Wheat Infected with
Stripe Rust and the Remote Sensing Mechanism of Disease Incidence (in Chinese). Journal of Anhui Agricultural Sciences 32,
132–134 (2004)
Zhang, Y.P., Guo, J.B., Wang, S., Wang, H.G., Ma, Z.H.: Relativity Research on Near ground and Satellite Remote Sensing
Reflectance of Wheat Stripe Rust (in Chinese). ActaPhytophylacica Sinica 36, 119–122 (2009)
Liu, L.Y., Song, X.Y., Li, C.J., Qi, L., Huang, W.J., Wang, J.H.: Monitoring and Evaluation of the Diseases and Yield Winter Wheat
from Multi-temporal Remotely-sensed Data (in Chinese). Transactions of the CSAE 25, 137–143 (2009)
Nagarajan, S., Seibold, G., Kranz, J., Saari, E. E., and Joshi, L. M. 1984. Monitoring wheat rust epidemics with the Landsat-2
satellite. Phytopathology 74:585-587.
Kanemasu ET, Niblett CL, Manges H, Lenhert D, Newman MA (1974) Wheat: its growth and disease severity as deduced from
ERTS-1. Remote Sens Environ 3:255–260
Zhang, J.C., W.J. Huang, J.Y. Li, G.J. Yang, J.H. Luo, X.H. Gu and J.H.Wang, 2011. Development, evaluation and application of a
spectral knowledge base to detect yellow rust in winter wheat. Precis. Agric., 12: 716–731
Chen X., J. Ma, H. Qiao, D. Cheng, Y. Xu and Y. Zhao (2007) Detecting infestation of take-all disease in wheat using Landsat
Thematic Mapper imagery. International Journal of Remote Sensing. 28: 5183-5189
Franke, J. and G. Menz, 2007. Multi-temporal wheat disease detection by multi-spectral remote sensing. Precis. Agric., 8:161–172
Mirik, M., Jones, D. C., Price, J. A., Workneh, F., Ansley, R. J., and Rush, C. M. 2011. Satellite remote sensing of wheat infected by
Wheat streak mosaic virus. Plant Dis. 95:4-12.
Zhihao Q, Minghua Z, Thomas C, Wenjuan L and Huajun T (2003) Remote Sensing Analysis of Rice Disease Stresses for Farm
Pest Management Using Wide-band Airborne Data. International Geosciences and Remote Sensing Symposium, IV: 2215 - 2217,
July 21-25, 2003, Toulouse, France
Nutter F. W., Jr. G. L. Tylka, J. Guan, A. J. D. Moreira, C. C. Marett, T. R. Rosburg, J. P. Basart and C. S. Chong (2002) Use of
Remote Sensing to Detect Soybean Cyst Nematode-Induced Plant Stress. Journal of Nematology 34(3):222–231.
Ranganath BK, Pradeep N, Manjula VB, Gowda B, Rajanna MD, Shettigar D, Nageswar Rao PP (2004) Detection of diseased
rubber plantations using satellite remote sensing. J Remote Sens 32(1):49–57
Apan A, Held A, Phinn S, Markley J (2004) Detecting sugarcane orange rust disease using EO-1 Hyperion hyperspectral imagery.
Int J Remote Sens 25:489–498
Blake W, Hongjie X and Paul J (2005) Early detection of oak wilt disease in quercus ssp.: A hyperspectral approach. Pecora 16
“Global Priorities in Land Remote Sensing” October 23 – 27, 2005 * Sioux Falls, South Dakota, USA.
Related flashcards

Languages of Morocco

19 cards

Languages of Mali

46 cards

Japanese martial arts

19 cards

World economy

12 cards

Languages of Ukraine

15 cards

Create Flashcards