Computer application in pest forecasting

SCOPE AND PROSPECTS OF
COMPUTER APPLICATION IN
PREDICTION AND
FORECASTING OF PEST
OUTBREAK
BY : JAYANT YADAV, CCSHAU, HISAR, HARYANA

 These predictions help in forewarning the
growers/farmers to take timely and judicious
intervention
2
Prediction
Pest
Outbreaks
Stages of
Pest
Pest
Population

 Forecasting is the prediction of severity of pest population
which can cause economic damage to the crop.
 Insect forecasting service may thus serve:
To predict the forth coming infestation level of the pest.
To find out the critical stage at which the application of
insecticides would afford maximum protection.
• The forecasting of pests guides the farmers about the
timing and biology of insect incidence, and to
eliminate blanket application, reduce pesticide
amounts and achieve quality results.
• The farmers can take to timely action of applying
various pest control measures to harvest maximum
returns.

Pest forecasting may be divided into two categories:-
1. Short-term forecasting : It covers a particular season or one
or two successive seasons only and is usually sampled from a
particular area within a crop using appropriate sampling
technique and the relationship is established between weather
data and progress in pest infestation.
e.g. Wheat grain aphid, Sitobion avenae (Fabricius) based on
multiple regressions
2. Long-term forecasting : These are based on possible effect of
weather on the pest population and cover a large area or by
extrapolating from the present population density into future.
e.g. Management strategies for Brown Planthopper, Nilaparvata
lugens (Stal) and White-backed Planthopper, Sogatella
furcifera (Horvath)

 The application of the computer in agriculture
research originally exploited for the conversion of
statistical formula or complex model in digital form
for easy and accurate calculation which are found
relatively tedious in manual calculation.
 Recently remote sensing and geographic information
system has place a major and crucial role in agriculture
research especially in the field of yield prediction,
suitability of soil for particular crop, and site specific
resource allocation of agriculture inputs, etc.

 Remote sensing refers to the process of gathering
information about an object, at a distance, without
touching the object itself.
 Certain phenomenon, which cannot be seen by human eye,
can be observed through remote sensing techniques i.e. the
trees, which are affected by disease, or insect-pests attack
can be detected by remote sensing techniques much before
human eyes see them.
 Geographical Information System is a computer-based
information system that can acquire spatial data from a
variety of sources, change the data into useful formats,
store the data, and retrieve and manipulate the data for
analysis.

 Remote sensing technology has long been used for
monitoring insect infestation in field crops.
 It is based on the principle that the absorbance and
reflectance of plants in response to pest attack
changes and these changes are recorded by a device
from far away.
 The early identification of damage due to two spotted
spider mite, Tetranychus urticae, on greenhouse
pepper can be obtained by multispectral means. As it
can be spectrally detected in the reflectance of the
visible and near – infrared regions.

 Database management has been widely used in pest
identification, managing pest monitoring data, population
modelling, exploring control strategies, biology of pests and
decision support system to take decision for adopting
intervention for pest management in field which are important
in IPM.
 The database has been developed in the form of CD-ROM,
internet based decision support systems, expert systems etc.
 The National Centre for Integrated Pest Management (NCIPM),
New Delhi has developed database management system
(www.ncipm.org.in/agroweb), where the information is available
on pest, disease management, nutritional deficiency and
physiological disorder in different crops grown in India.

 Decision support system are computer based information
system designed in such a way that they help farmers to
select one of the many alternative solutions to a pest
problem.
 In IPM, DSSs are widely used for identification,
recommendation of insecticides, emergence of insect pests
outbreaks.
 The DSS supports adoption of IPM technologies to reduce
the cost of production and minimizing environmental and
public hazard.
• DSS has also developed at NCIPM, which provides
information on pest management systems, forecasting of
pests, and distribution maps.

 A number of decision support system have been developed
in different countries on different crops.
 Codling Moth Information Support System (CMISS)
http://ipmnet.org/codlingmoth/ site contains various
knowledge bases, databases, phenology models, and links
to worldwide resources on codling moth.
 A spatial DSS MedCila was developed in Israel for
controlling Mediterranean fruit fly, Ceratitis capitata, in
citrus in 2004. The recommendations of MedCila are
generally accepted and reduce the unnecessary sprays.

Decision
support
system
Crop Target Pest Country Function
EntomoLOGI
C
Cotton Helicoverpa
spp., two
spotted spider
mite
Australia Predicting
future pest
numbers
proPlant Oilseed rape Cabbage stem
flea beetle,
Psylliodes
chrysocephala
,
Rape stem
weevil,
Dasineura
brassicae
Europe Predicts the
start of pest
infestation
and provides
selection, date
and rate of
chemical
application
SIMLEP Slash and Pine
seed orchards
Colorado
potato beetle,
Leptinotarsa
decimlineata
Slovenia Forecasted the
first
occurrence of
young and old
larvae
(www.dssresources.com)12

 Another computer programme based on database
management is Expert Systems (ES)
 Though both DSS and ES seek to improve the quality
of the decision, these are distinguished based on
objectives , operational differences, users and
development methodology.
 The different Expert Systems (ES) have been designed
which help in identification of insect-pests, estimating
risk from pests, control measure recommendations
etc.

Computer based Expert Systems (EXS)
Rice BPH Expert System
Constructed to diagnose and
pesticide recommendations for brown
plant hopper in Zhejiang Province,
China
Help in the identification of research,
surveillance and monitoring needs
(Holt et al 1990)
SGA Pro
Stored Grain Advisory Pro was developed to provide insect pest
management information for wheat stored at commercial elevators
The program uses a model to predict future risks based on current
insect density, grain temperature and moisture
(Flinn et al 2007)
14

Expert
systems
Crop Target pest Country Function
TEAPEST Tea Pests of tea India Identify and
suggest
appropriate
control
measures
SOYPEST(Soybe
an Pest Expert
System)
Soybean 120 pests India Idetification and
decision in IPM
GyMEs(Gypsy
Moth Expert
System)
Different plant
species in
forests
Gypsy Moth North America Estimate risk to
forest from
pests.

 Injudicious and untimely application of pest control
measures has led to situation of repeated pest outbreaks,
pesticide resistance, pest resurgence and secondary
pest outbreak.
 The various decision support systems and expert systems
are providing quite useful information for enhancing
awareness among farmers about insect pests of crops,
requirement of crops, taking decisions to initiate control
measures etc.
 A close collaboration between computer programmers and
plant protection scientists is essential to develop farmer
friendly systems.

 Computer based information systems have changed
the way we do research.
 They have enhanced our ability to identify and solve
problems and to perform tasks that are beyond our
physical ability.
 Information system technology, bioinformatics, and
nanotechnology no doubt will continue to provide new
horizon to us in the years to come.

Computer application in pest forecasting

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Computer application in pest forecasting