International Journal of Advancements in Research & Technology, Volume 3, Issue 1, January-2014
ISSN 2278-7763
74
Wireless Sensor Network based Forewarning Models for
Pests and Diseases in Agriculture – A Case Study on
Groundnut
Santosh Sam Koshy1, Yesho Nagaraju1, Sowjanya Palli1, Y. G. Prasad2, Naveen Pola2
1
Team Embedded, Centre for Development of Advanced Computing, Hyderabad, India; 2Department of Entomology, Central Research Institute for
Dryland Agriculture, Hyderabad, India.
Email: santoshk@cdac.in, ynagaraju@cdac.in, sowjanyap@cdac.in, ygprasad@crida.ernet.in, naveen_pola@yahoo.co.in
ABSTRACT
In Agriculture, microclimate plays an important role in the growth and outbreak of pests and diseases. Wireless Sensor
Networks (WSN) enables the acquisition of both microclimate and macroclimate weather data from agricultural farms thereby
facilitating new insights into the crop-weather dynamics in and around the crop canopy. In this paper, we present the results of
an open farm deployment of WSN for the groundnut crop, with emphasis on weather based Pest and Disease Management.
Having implemented two decision support advisory models for a groundnut disease, and one model for a groundnut pest, we
highlight the importance of the microclimate over the macroclimate. We also discuss the cost benefit of the WSN based
Advisory over the farmer's practice and other standard practices.
Keywords : Ubiquitous Computing, Wireless Sensor Networks, Micro Climate, Leaf Spot, Leaf Miner, Groundnut, Randomized
Block Designs
1 INTRODUCTION
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Ubiquitous Computing (UbiComp), the third wave of computing, follows the eras of mainframe and personal computing
[13]. Ubicomp is considered as the age of Calm Technology
[14] where technology recedes into the background while rendering its supportive services in an unobtrusive way. Wireless
Sensor Network (WSN) is one step in this direction, enabling
Ubiquitous Computing to proliferate our daily spaces [1].
Conceptually speaking, WSN combines various technologies
like Sensing, Processing and Wireless Communication into a
system architecture that facilitates the interfacing of the physical world with Cyberspace [9].
Agricultural practices need to address problems like climate change, land infertility, diminishing yields, and rampant
pest outbreaks [17]. The knowledge of weather helps in addressing a few of these problem areas satisfactorily. Automatic
Weather Stations (AWS) measuring parameters like Temperature, Relative Humidity, Rainfall, Solar Radiation and Wind
Speed & Direction, provide macroclimate information [4]. Agricultural research also emphasizes the need for understanding microclimate within the crop canopy by measuring parameters like Leaf Wetness, Soil Moisture & Temperature and
Canopy Temperature & Humidity [21].
Groundnut in India, is cultivated in low to moderate
rainfall zones [6]. The crop age typically varies between 90-130
days. It is grown in two seasons Kharif and Rabi during a calendar year. The Kharif season (June-September) is characterized by rain fed agriculture and during the Rabi season (November- February), fields are irrigated. Investigations reveal
Copyright © 2014 SciResPub.
that Leaf Miner is considered to be one of the major pests, of
the groundnut crop. Temperature plays an important role in
the pest’s growth, as the pest requires the accumulation of
fixed amounts of heat units, to pass from one stage to the next
of its life cycle [12]. The conditions favorable for leaf miner
growth are, long dry spells resulting in high Temperature and
low humidity [6].
Groundnut is also prone to attack by numerous diseases.
Among fungal foliar diseases, only a few are economically
important in India such as Leaf Spot (early and late) and Rust.
These are widely distributed and can cause yield losses in susceptible genotypes to the extent of 70% [19]. Weather conditions congenial for occurrence of early and late Leaf Spot are
rainfall, moisture causing leaf wetness and temperature [6].
WSN facilitates the aggregation of microclimate information from agricultural fields by installing sensors within the
crop canopy at various locations in the field. This microclimate
information supports the analysis of various factors that influence crop and pest growth thereby aiding the development of
decision support advisory models. The decision support advisories help farmers to make better decisions in crop management. WSN’s sphere of influence in agriculture, encompass
areas like irrigation, pest & disease management, drought
analysis & early warning, precision farming etc.
The paper is divided into six sections. Section 1 briefly reviews some agricultural experiments using WSN. Section 3
describes the Ubiquitous Agriculture System Architecture and
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International Journal of Advancements in Research & Technology, Volume 3, Issue 1, January-2014
ISSN 2278-7763
the Decision Support Advisory Models developed as part of
the research. The Observations and Results are analyzed in
Section 4, while the Conclusions are summarized in Section 5,
followed by the Future Work, listed in Section 6
75
one-shot sensor information. In normal mode of operation, the
parameters are sensed once in every hour according to the
domain requirements, but can also be configured to sense at
intervals as low as 10 seconds.
2 RELATED WORK
An experiment in field crop production, Lofar Agro, deals
with fighting phytophthora in a potato field. To monitor Relative Humidity, Temperature and Leaf Wetness, which are important indicators to the development of the disease, the potato field was instrumented with wireless sensors [16]. In another experiment Automated Agriculture System (A2S), a WSN
was deployed in greenhouses with melon and cabbage in
Dongbu Handong Seed Research Center. A2S was used to
monitor the growing process, and control the illumination
within the greenhouses [1]. A prototype, consisting of a wireless network of ground-sensors periodically records soil moisture, temperature, humidity and atmospheric pressure in the
field environment. Data sensed was used for forecasting,
forewarning and ultimately to increase productivity [7]. Similarly, a WSN was deployed to monitor weather and environmental conditions that affect the phenological stages of various grapevine varieties in different countries [11]. Intel researched WSN in vineyards and worked out methods of better
management based on the ethnographic research [2].
3
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SYSTEM ARCHITECTURE
As part of the National Initiative in Ubiquitous Computing, a
pilot, WSN based, Ubiquitous Agriculture (u-Agri) system is
developed and deployed in groundnut research farms at Hyderabad, India. The project is a collaborative research effort
between the Centre for Development of Advanced Computing
(C-DAC) and the Central Research Institute for Dryland Agriculture (CRIDA). The aim of the project is to investigate the
effect of microclimate on pests and diseases in groundnut and
provide forewarning advisories.
3.1
Fig. 1: u-Agri System Architecture
Description
The system architecture (Fig. 1) comprises three components
namely Farm Site (FS), Gateway Subsystem (GS) and Remote
Administration System (RAS). The FS consists of WSN motes
deployed in the groundnut field. The GS aggregates sensed
weather data from the FS and stores it on the RAS. The RAS
utilizes WSN weather data for data analysis and decision support advisory models. The detailed description of the system
architecture is as follows.
3.1.1 Farm Site (FS)
The WSN motes in the FS include IRIS motes, purchased from
Memsic Inc. [22] as well as motes developed in-house, based
on CC2430 SoC and MSP430. The motes are programmed to
run a TinyOS-2.x [23] application for sensing and multihop
routing of sensed data to the GS. The motes also provide a
feature of control information dissemination, that enables an
end-user to configure parameters like sensing intervals, query
the mote for its health specific information and, also request
Copyright © 2014 SciResPub.
WSN data packets are created whenever a sensor is sampled, based on its configured periodicity. These packets are
multi-hopped to the GS using the Collection Tree Protocol
(CTP), developed in TinyOS. A number of other routing protocols like TinyAODV, Multihop LEPS and Static Routing
were also integrated and tested during the field trials, but CTP
was chosen for its satisfactory performance.
In order to achieve ultra-low power network operation,
which is a well-known challenge in outdoor deployments, it is
mandatory that the mote radio be duty-cycled in a coordinated manner. Time-synchronization algorithms are therefore
required, to perform coordinated network sleeping. The constrained resources on a mote due to its low processing power
and memory, curtail this process. Further, integration of these
algorithms with existing routing algorithms proves challenging. A Time Division Multiple Access (TDMA) based approach for network coordination has been developed inhouse, which provides both multi-hop routing and control
information dissemination. Simulation results have proven
that ultra-low power consumption is achieved through 1 percent radio duty cycling. This algorithm is currently being integrated into the u-Agri system for field trials.
Two classes of motes are deployed, which enable microclimate and macroclimate monitoring. The microclimate sensing motes are deployed in the crop canopy area and are interfaced with sensors like Temperature, Relative Humidity,
Leaf Wetness and Soil Moisture & Temperature. Temperature and Relative Humidity is measured at a domain specified standard height of 1.5 meters and Leaf Wetness, Soil
Moisture & Temperature is measured at crop canopy. These
motes are separated by a distance of 100 meters and form a
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International Journal of Advancements in Research & Technology, Volume 3, Issue 1, January-2014
ISSN 2278-7763
multi-hop network to route sensed information to the GS. A
single mote is deployed to sense the macroclimate and is interfaced with coarsely varying sensors like Solar Radiation, Rainfall and Wind Speed & Direction.
76
deployed in an area of 4 acres, while in Ananthapur, which is
considered as the groundnut belt of India, 25 micro climate
motes, developed in-house, are deployed in an area of 10 hectares. The Kadiri deployment has 10 motes, covering 20 acres
of land. Presently, the motes are encased in wooden boxes
with air vents popularly known as Stevenson's screens, with
the Temperature & Relative Humidity sensors located within.
The mote unit is powered by a battery that is charged by a
solar panel, making the setup totally standalone.
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Fig. 2: Deployment at Hyderabad, AP
Fig. 4: Deployment at Kadiri, AP
Fig. 5: Randomized Block Designs
Fig. 3: Deployment at Ananthapur, AP
The WSN deployment is carried out in open farm conditions, in 3 different locations namely Hyderabad (Fig. 2), Ananthapur (Fig. 3) and Kadiri (Fig. 4). In the Hyderabad deployment, 6 micro climate motes and 1 macro climate mote are
Copyright © 2014 SciResPub.
The field is divided into blocks (Fig. 5), and each block is
administered pest and disease treatments based on six strategies (T1 – T6). This representation, referred to as Randomized
Block Design (RBD) [18], aids scientific treatment analysis of
the crop, leading to cost benefit analysis.
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International Journal of Advancements in Research & Technology, Volume 3, Issue 1, January-2014
ISSN 2278-7763
3.1.2 Gateway Subsystem (GS)
The GS is a bridge between FS and RAS. The GS comprises a
WSN Gateway and a RAS Interface Unit. The WSN Gateway
runs TinyOS-2.x with routing and the dissemination components. The RAS Interface Unit is a single board computer,
which runs the Linux OS. It is programmed to convert raw
sensor data to standard engineering units and store on a local
database. This weather data is periodically uploaded to the
RAS through an Internet modem. The GS is currently enclosed
in a wooden box similar to the FS motes in order to protect it
from sun and rain and is powered by mains, backed up by an
Uninterrupted Power Supply (UPS) system.
At present, a micro-controller based solar powered low
power gateway is being designed and developed in-house to
operate in a standalone mode, without mains power. The design challenge restricts the average power consumption to 1
Watt, considering continuous operation due to networking
requirements. The gateway is based on the ARM Cortex M-3
processor, running the CooCox Operating system and interfaces with a WSN mote and GSM modem.
3.1.3
Remote Administration System
77
groundnut Leaf Spot and one model for groundnut Leaf Miner
are implemented under the supervision and guidance of domain experts from CRIDA. The Leaf Wetness Index (LWI)
model [3] and Temperature-Relative Humidity Index (T/RH)
model [8] forewarn Leaf Spot disease development. The development life cycle of groundnut leaf miner is modeled using
Growing Degree Days (GDD) [12]. The following factors are
considered in the model development.
(a) The crop age is 130 days from sowing to harvest.
(b) Disease incidence is low in the initial 50 days.
(c) A window of 14 days is maintained between fungicide
sprays.
3.2.1 Leaf Wetness Index Advisory
Infection of groundnut by pathogens, causing early and late
Leaf Spot diseases, is strongly influenced by accumulated Leaf
Wetness spells each day. The infection is severe and spray is
advised when the cumulative 7-day Wetness Index (WI) exceeds a threshold of 2.3 and the disease incidence exceeds
10%. Leaf Wetness Index (LWI) for a day is computed from
Leaf Wetness hours. If Wetness Hours (WH) in a day is 20 or
less, then WI is set to
WI = WH/20
(1)
and when greater than 20, the WI is derived from the expression
WI = 4.5 – 0.175*WH
(2)
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Fig. 6: Remote Administration System
The RAS hosts a web server and a database. Weather data
from FS is stored on the database and is provided as input to
data analysis and decision support advisory models. Registered users receive weather based decision support advisories
for Pest and Disease Forewarning as Short Message Service
(SMS) messages. Fig. 6 illustrates the modules developed on
the RAS as part of the u-Agri system.
3.2
Decision Support Advisory Models
Two weather based decision support advisory models for
Copyright © 2014 SciResPub.
3.2.2 T/RH Index Advisory
The model renders a day-to-day forewarning for groundnut
Leaf Spot assuming the availability of hourly observations of
Temperature and Relative Humidity (RH) for previous five
days. Number of hours with RH > 95% and the minimum
Temperature during those hours are used for calculating
T/RH index. Hours of RH > 95% are limited between 2 and
20, while the Minimum Temperature is limited between 62F
and 80F. T/RH index is derived from the graph plotted between number of Hours with RH > 95% and minimum Temperature [5]. The model is implemented with an approximation suggested by similar experiments conducted by researchers at Oklahoma State University [15]. The RH threshold is
therefore reduced to 80% (measured at 1.5 meters) rather than
95% (at canopy) as suggested in the original citation. The
T/RH model therefore contributes to the macroclimate analysis of the Leaf Spot disease.
3.2.3 Growing Degree Day Model
A Degree Day (DD) corresponds to the difference of one degree between mean temperatures each day, and a reference
Temperature. The reference Temperature is a threshold Temperature that governs the development of the pest. This value
varies with different stages of the pest life cycle. Each stage
requires the accumulation of a fixed number of DDs for transition to the next stage of development. The date to begin accumulating DDs, known as the bio-fix, varies with the species. A
leaf miner completes its life cycle in 660 DDs (Table 1), above
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International Journal of Advancements in Research & Technology, Volume 3, Issue 1, January-2014
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threshold temperature. Calculated DDs serve as the base to
predict whether the pest is in egg or larval stage and thus assists in timing pesticide sprays.
Table 1. Threshold Temperatures and Degree Days for Each
Stage in The Leaf Miner's Life Cycle
Stage
Temperature
Threshold (°C)
Degree Days
Egg
12.4
60
Larva
11.3
327
Pupae
14.7
72
Adult
3
202
4.2
WSN Advisory Observations
4.2.1 Leaf Spot during Kharif & Rabi 2009
Leaf Wetness and T/RH indices were calculated each day for
the weather data stored in the RAS database. Advisories were
issued after 50 days of crop sowing. A favorable advisory and
an acceptable grade of disease incidence (as mentioned in Table 3) were considered for spraying fungicide.
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4
OBSERVATIONS & RESULTS
4.1
Field Observations
4.1.1 Kharif and Rabi 2008
During Kharif and Rabi 2008, prior to the deployment of the
u-Agri System, the crop was maintained untreated throughout
the season to record the natural pest and disease incidence
pattern. The dates at which the damage (caused by Leaf Miner) reached a peak stage are represented in the Table 2. The
table depicts that the second generation of the Leaf Miner pest
occurs during a critical stage in the crop life cycle. The peak
population of the pest during this period is critical to the yield.
Table 2. Leaf Miner Activity Records For Kharif And Rabi
2008 Seasons
Groundnut Leaf Miner
Season
Sown
Date
Pheromone
Trap Catch
Field
Population
Initia
tion
Date
Peak
Date
Initiati
on
Date
Peak
Date
Kharif
28/06
31/7
(21)
27/8
(101)
15/7
(42)
8/9
(272)
Rabi
31/10
12/12
(21)
27/1
(161)
11/12
(12)
6/2
(172)
2
Leaf Spot disease initiation was triggered by Temperatures <
25 C during the day and around 20 C at night, coupled with
RH > 90% during the early morning observation and around
70% during the noon observations. The disease progress rate
(slope) was highest during prolonged wet spells. In the same
year during the Rabi season significant disease incidence was
not observed.
4.1.2 Kharif & Rabi, 2009
Due to unprecedented drought conditions that prevailed during the 2009 Kharif season, sowing was delayed to early July.
Weekly assessment of the Leaf Spot disease was done, on 3
randomly selected plants from the entire plot, a week following the appearance of the disease. All the groundnut plants
surrounding the Leaf Wetness sensor were inspected for disease incidence. The disease infection was graded on a scale of
1-5 as mentioned in the Table 3.
The first adult catch in Pheromone traps [20] sets the biofix
date for the model. The GDD is thereafter calculated indicating the arrival of the next larval stage based on the current
Temperature values and normalized historical data. The historical data compliments the real time data for calculation of
GDD and aids prediction of the growth pattern for the pest
thereby leading to accurate control action.
1
78
Number of Adult Moths / trap
Number of Larvae/ 10 Plants
During the Kharif season, observations indicated that
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Table 3. Criteria For Calculating The Leaf Spot Infection
Grade
Leaf Spot
Infection
Rating/Grade
Number of Lesions
observed on randomly
chosen plants
Area Covered
with infection
(%)
1
<5
<1
2
05 – 20
1–5
3
20 – 50
5 – 20
4
50 - 100
20 – 50
5
>100
> 50
As depicted in Table 4, field observations were done at
different crop ages. During the Kharif season, it was observed
that Leaf Wetness Index (LWI) & Temperature-Relative Humidity Index (T/RH) values exceeded the threshold at 58, 61
and 86 days of crop age which coincided with initial disease
appearance and scouted disease incidence grade (as shown in
Fig. 7). However, fungicide spray was not recommended since
the disease incidence had not crossed the acceptable threshold.
The unprecedented drought conditions could be linked to the
slow onset of the disease. A precautionary spray was done at
the crop age of 86 days following the WSN Advisory. A
dormant window of 14 days was maintained following fungicide spray and therefore, the models did not issue advisories
during this period (represented as ‘NA’ in Table 4).
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79
ity build up occurs at the crop canopy, there is not much
change at the standard height (i.e. 1.5 meters, where T/RH is
sensed). The experiment during the Rabi 2009 season indicates
that the microclimate introduces greater dynamism in our understanding of pest and disease life cycle and is therefore an
important consideration for pest and disease forewarning.
Fig. 7: Disease Incidence Grade vs Leaf Wetness Index
As depicted in Table 4, field observations were done at
different crop ages. During the Kharif season, it was observed
that Leaf Wetness Index (LWI) & Temperature-Relative Humidity Index (T/RH) values exceeded the threshold at 58, 61
and 86 days of crop age which coincided with initial disease
appearance and scouted disease incidence grade(as shown in
Fig. 7). However, fungicide spray was not recommended since
the disease incidence had not crossed the acceptable threshold.
The unprecedented drought conditions could be linked to the
slow onset of the disease. A precautionary spray was done at
the crop age of 86 days following the WSN Advisory. A
dormant window of 14 days was maintained following fungicide spray and therefore, the models did not issue advisories
during this period (represented as ‘NA’ in Table 4).
4.2.2 Leaf Miner during Kharif & Rabi 2009
DDs are calculated using the WSN weather data following the
first adult catch in the pheromone traps erected in the field.
The model provided information about the current stage in
the life cycle of the pest. It also aided the prediction of IInd
Generation of the pest, which is considered critical for the
crop. The prediction of growth pattern was achieved by integrating the real time WSN data with normalized historical
data from the agro-meteorological observatory. Table 5 lists
the pest growth pattern based on the accumulated DDs.
4.3
Cost Benefit Analysis
Cost benefit analysis was conducted for individual Randomized Block Designs (RBD) based on the number of fungicide
treatments applied and yield obtained in that RBD for the Rabi
Season 2009.
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While the LWI model was deployed to sense the microclimate, the T/RH model with an approximation suggested by
agricultural scientists [15] was deployed to sense the macroclimate. Comparing the advisories issued by both models, we
may conclude that there is close similarity in the timing of the
advisories. We also observe that the LWI model issued three
additional favorable advisories while the T/RH model remained unfavorable. This dissimilarity may be attributed to
the effect of the microclimate over the macroclimate.
The Rabi crop was sown early November 2009. The crop
was raised under irrigated conditions (using sprinklers) and
the build up of disease was high during the season. The disease progress had higher slope value in un-sprayed plots.
While the T/RH index values exceeded threshold only once
during the entire season, the LWI index values were higher
than the threshold (i.e. 2.3) for much of the season (Table 4).
Fungicides were applied based on the LWI advisory and the
Leaf Spot grade.
Between the two models, there was minimal similarity
during the Rabi season. This clearly brings out the variance
between the microclimate and the macroclimate. This variance
may be attributed to the fact that sprinklers irrigate the Rabi
season. Due to the nature of the sprinklers, though the humidCopyright © 2014 SciResPub.
Results of the analysis have shown that the cost benefit
ratio was higher in RBDs, which followed the WSN advisorybased treatments. From the Table 6, it is evident that RBDs
with Complete Protection scheduled eight fungicide sprays,
while WSN RBDs scheduled only four, without affecting the
yield significantly. In comparison to the Farmer’s Practice, the
WSN RBD scheduled an additional spray but provided significantly higher yield.
5
CONCLUSIONS
From the discussions we may conclude that the microclimate
plays an important role in the growth and outbreak of pests
and diseases. Having implemented and compared the results
of one macro-climate advisory (T/RH Model) and one microclimate advisory (LWI Model) for Leaf Spot disease, we may
conclude that during the Kharif season (rain fed), both models
performed similarly and could be used interchangeably. But
the experiment conducted during the Rabi Season (sprinkler
irrigation) clearly reveals that the macroclimate model fails to
predict the disease outbreak since the humidity remains unaffected by the sprinkler irrigation while the microclimate
Table 6. Cost Benefit Analysis of WSN Advisory
Rabi 2009
Number
of
Treatments2
FP1
CP1
WA1
NP1
3
8
4
0
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International Journal of Advancements in Research & Technology, Volume 3, Issue 1, January-2014
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Yield
(Kg/Ha/RBD)
Cost
Benefit
Ratio3
1400
1750
1650
1318
1.1
2.18
3.35
-
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FP – Farmer's Practice; CP – Complete Protection;
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FUTURE WORK
WSN based Pest and Disease Advisories will be correlated
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ACKNOWLEDGMENTS
We thank the Department of Electronics & Information Technology (DeitY), Ministry of Communications & Information
Technology, Government of India, for their continual support
towards research in Ubiquitous Computing. A word of appreciation towards the Central Research Institute for Dryland
Agriculture for their collaboration and domain expertise,
without which, this research would not have been fruitful.
Finally, we thank the Centre for Development of Advanced
Computing encouraging and motivating us during our work.
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From
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June, 2012.
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From
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[22] http://www.memsic.com
[23] http://www.tinyos.net
Table 4. WSN Advisories Issued For Leaf Spot During Kharif & Rabi 2009
Kharif 2009
IJOART
Crop Age
Leaf Spot Grade
LWI Advisory
51
55
58
61
63
68
71
74
77
86
89
92
95
99
102
0.1
0
0
0.4
0.8
0.5
0.95
0.75
0.6
1.35
1.35
1.6
3.25
3.1
3
1
Yes
No
Yes
Yes
Yes
No
No
No
No
Yes
NA
NA
NA
NA
Yes
No
No
Yes
Yes
No
No
No
No
No
Yes
NA
NA
NA
NA
No
Similarity between Models
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
-
-
-
-
No
Spray Fungicide based on
WSN Advisory and Disease
Grade
No
-
No
No
No
-
-
-
-
Yes
-
-
-
-
No
T/RH Advisory
Rabi 2009
Crop Age
52
58
62
66
72
77
81
83
87
93
100
107
114
118
124
Leaf Spot Grade
0
0
0
3.3
3.3
2.7
3
2.3
5
4.7
5
4.3
5
5
4.7
LWI Advisory
Yes
Yes
Yes
Yes
Yes
NA
NA
NA
Yes
ND2
ND
No
Yes
NA
NA
T/RH Advisory
No
No
No
No
Yes
NA
NA
NA
No
ND
ND
No
No
No
No
Similarity between Models
No
No
No
No
Yes
-
-
-
No
-
-
Yes
No
No
No
Spray Fungicide based on
WSN Advisory and Disease
Grade
No
No
No
No
Yes
-
-
-
Yes
-
-
-
Yes
-
-
1
Not Applicable
2
No Data
Table 5. GDD Calculations Using The WSN Based Leaf Miner Advisory Model
Copyright © 2014 SciResPub.
IJOART
International Journal of Advancements in Research & Technology, Volume 3, Issue 1, January-2014
ISSN 2278-7763
82
Kharif 2009
Generation
I (July 2)
II (Aug 7)
Standard Week1
Accumulated GDD
Egg Stage2
Larval Stage2
Pupal Stage2
27
59.59
1
22
27
28
190.56
-
13
18
29
316.25
-
5
10
30
446.24
-
-
1
31
574.25
-
-
-
32
41.99
2
25
30
33
165.24
-
16
21
34
281.20
-
8
13
35
388.33
-
1
6
36
468.1
-
-
-
IJOART
Rabi 2009
I (Nov 7)
II (Jan 2)
45
13.45
4
36
44
46
117.36
-
27
35
48
309.92
-
8
16
50
469.65
-
-
-
1
43.39
2
34
41
2
119.95
-
26
33
3
209.35
-
17
24
4
280.93
-
11
17
5
355.08
-
4
10
6
470.3
-
-
1
1 Standard
2 Refers
Week refers to one week in a calendar year, beginning as 1 in Jan and ending as 52 in Dec
to the number of days remaining for completion of the stage
Copyright © 2014 SciResPub.
IJOART