Wireless Sensor Network based Forewarning Models for Pests ... · Wireless Sensor Network based...

Wireless Sensor Network based Forewarning Models for

Pests and Diseases in Agriculture – A Case Study on

Groundnut

Santosh Sam Koshy1, Yesho Nagaraju

1, Sowjanya Palli

1, Y. G. Prasad

2, Naveen Pola

2

1Team Embedded, Centre for Development of Advanced Computing, Hyderabad, India; 2Department of Entomology, Central Research Institute for

Dryland Agriculture, Hyderabad, India.

Email: [email protected], [email protected], [email protected], [email protected], [email protected]

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

Ubiquitous Computing (UbiComp), the third wave of compu-ting, 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 ren-dering 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 physi-cal world with Cyberspace [9].

Agricultural practices need to address problems like cli-mate change, land infertility, diminishing yields, and rampant pest outbreaks [17]. The knowledge of weather helps in ad-dressing a few of these problem areas satisfactorily. Automatic Weather Stations (AWS) measuring parameters like Tempera-ture, Relative Humidity, Rainfall, Solar Radiation and Wind Speed & Direction, provide macroclimate information [4]. Ag-ricultural research also emphasizes the need for understand-ing microclimate within the crop canopy by measuring pa-rameters 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 cal-endar year. The Kharif season (June-September) is character-ized by rain fed agriculture and during the Rabi season (No-vember- February), fields are irrigated. Investigations reveal

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 sus-ceptible genotypes to the extent of 70% [19]. Weather condi-tions congenial for occurrence of early and late Leaf Spot are rainfall, moisture causing leaf wetness and temperature [6].

WSN facilitates the aggregation of microclimate infor-mation 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 influ-ence crop and pest growth thereby aiding the development of decision support advisory models. The decision support advi-sories help farmers to make better decisions in crop manage-ment. 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 re-views some agricultural experiments using WSN. Section 3 describes the Ubiquitous Agriculture System Architecture and

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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

2 RELATED WORK

An experiment in field crop production, Lofar Agro, deals with fighting phytophthora in a potato field. To monitor Rela-tive Humidity, Temperature and Leaf Wetness, which are im-portant indicators to the development of the disease, the pota-to field was instrumented with wireless sensors [16]. In anoth-er 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 wire-less network of ground-sensors periodically records soil mois-ture, temperature, humidity and atmospheric pressure in the field environment. Data sensed was used for forecasting, forewarning and ultimately to increase productivity [7]. Simi-larly, a WSN was deployed to monitor weather and environ-mental conditions that affect the phenological stages of vari-ous grapevine varieties in different countries [11]. Intel re-searched WSN in vineyards and worked out methods of better management based on the ethnographic research [2].

3 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 Hy-derabad, 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 Agri-culture (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 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 sup-port 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

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.

Fig. 1: u-Agri System Architecture

WSN data packets are created whenever a sensor is sam-pled, 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 pro-tocols 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 coordinat-ed manner. Time-synchronization algorithms are therefore required, to perform coordinated network sleeping. The con-strained 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 challeng-ing. A Time Division Multiple Access (TDMA) based ap-proach for network coordination has been developed in-house, which provides both multi-hop routing and control information dissemination. Simulation results have proven that ultra-low power consumption is achieved through 1 per-cent radio duty cycling. This algorithm is currently being inte-grated into the u-Agri system for field trials.

Two classes of motes are deployed, which enable micro-climate and macroclimate monitoring. The microclimate sens-ing motes are deployed in the crop canopy area and are inter-faced with sensors like Temperature, Relative Humidity,

Leaf Wetness and Soil Moisture & Temperature. Temper-ature and Relative Humidity is measured at a domain speci-fied 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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multi-hop network to route sensed information to the GS. A single mote is deployed to sense the macroclimate and is inter-faced with coarsely varying sensors like Solar Radiation, Rain-fall and Wind Speed & Direction.

Fig. 2: Deployment at Hyderabad, AP

Fig. 3: Deployment at Ananthapur, AP

The WSN deployment is carried out in open farm condi-tions, in 3 different locations namely Hyderabad (Fig. 2), An-anthapur (Fig. 3) and Kadiri (Fig. 4). In the Hyderabad de-ployment, 6 micro climate motes and 1 macro climate mote are

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 hec-tares. 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.

Fig. 4: Deployment at Kadiri, AP

Fig. 5: Randomized Block Designs

The field is divided into blocks (Fig. 5), and each block is

administered pest and disease treatments based on six strate-gies (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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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 compo-nents. 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 de-sign 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 inter-faces with a WSN mote and GSM modem.

3.1.3 Remote Administration System

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. Regis-tered 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

groundnut Leaf Spot and one model for groundnut Leaf Miner are implemented under the supervision and guidance of do-main 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 de-velopment 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) ex-ceeds 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 expres-sion WI = 4.5 – 0.175*WH (2)

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 be-tween number of Hours with RH > 95% and minimum Tem-perature [5]. The model is implemented with an approxima-tion suggested by similar experiments conducted by research-ers 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 analy-sis of the Leaf Spot disease.

3.2.3 Growing Degree Day Model A Degree Day (DD) corresponds to the difference of one de-gree between mean temperatures each day, and a reference Temperature. The reference Temperature is a threshold Tem-perature 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 transi-tion to the next stage of development. The date to begin accu-mulating 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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threshold temperature. Calculated DDs serve as the base to predict whether the pest is in egg or larval stage and thus as-sists 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

The first adult catch in Pheromone traps [20] sets the biofix date for the model. The GDD is thereafter calculated indicat-ing the arrival of the next larval stage based on the current Temperature values and normalized historical data. The his-torical 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.

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 Min-er) 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

Season Sown

Date

Groundnut Leaf Miner

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)

1 Number of Adult Moths / trap 2 Number of Larvae/ 10 Plants

During the Kharif season, observations indicated that

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 dur-ing 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 follow-ing the appearance of the disease. All the groundnut plants surrounding the Leaf Wetness sensor were inspected for dis-ease incidence. The disease infection was graded on a scale of 1-5 as mentioned in the Table 3.

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 Ta-ble 3) were considered for spraying fungicide.

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 Hu-midity 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 fungi-cide spray and therefore, the models did not issue advisories during this period (represented as ‘NA’ in Table 4).


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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 Hu-midity 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 fungi-cide spray and therefore, the models did not issue advisories during this period (represented as ‘NA’ in Table 4).

While the LWI model was deployed to sense the micro-climate, the T/RH model with an approximation suggested by agricultural scientists [15] was deployed to sense the macro-climate. 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 re-mained 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 dis-ease 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 humid-

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 un-derstanding of pest and disease life cycle and is therefore an important consideration for pest and disease forewarning.

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 inte-grating 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 Random-ized Block Designs (RBD) based on the number of fungicide treatments applied and yield obtained in that RBD for the Rabi Season 2009.

Results of the analysis have shown that the cost benefit ratio was higher in RBDs, which followed the WSN advisory-based 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 signif-icantly 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 micro-climate 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 unaf-fected by the sprinkler irrigation while the microclimate

Table 6. Cost Benefit Analysis of WSN Advisory

Rabi 2009

FP1

CP1

WA1

NP1

Number of

Treatments2 3 8 4 0


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Yield

(Kg/Ha/RBD) 1400 1750 1650 1318

Cost Benefit

Ratio3 1.1 2.18 3.35 -

1 FP – Farmer's Practice; CP – Complete Protection;

WA – WSN Advisory; NP – No Protection 2 Cost per spray per ha = 620/-

3 Calculated with reference to NP treatment. Ratio of

additional yield (@ 25/- per Kg) and Fungicide Cost

Successfully forewarns the disease outbreak. Moreover,

the cost benefit analysis reveals that reduction in the pesticide and fungicide usage is possible without affecting the yield in a significant way. Though the WSN costs have not been includ-ed into the cost benefit analysis, efforts are being made to min-imize the number of sensors and increase the area of coverage to make the system economically viable. WSN provides nu-merous opportunities of research in microclimate analysis, which is absent in the current agricultural scenario. Greater proliferation of WSN based systems will enable optimal strat-egies to be developed for better crop management. With Di-minishing Yields and Pest & Disease menaces being a world-wide concern, WSN provides a possible solution to smarter agriculture.

6 FUTURE WORK WSN based Pest and Disease Advisories will be correlated with Remote Sensing data to investigate the possibilities of increasing the coverage area of advisory. Collaborative re-search activities have been proposed with the National Re-mote Sensing Centre (NRSC) to develop tools and models in this area. This would strengthen the possibility of developing a cost effective system, which can be afforded by the farming community. In addition, the optimal number of WSN motes for one hectare of deployment will be worked out as part of our future objectives.

ACKNOWLEDGMENTS We thank the Department of Electronics & Information Tech-nology (DeitY), Ministry of Communications & Information Technology, Government of India, for their continual support towards research in Ubiquitous Computing. A word of appre-ciation 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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Table 4. WSN Advisories Issued For Leaf Spot During Kharif & Rabi 2009

Kharif 2009

Crop Age 51 55 58 61 63 68 71 74 77 86 89 92 95 99 102

Leaf Spot Grade 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

LWI Advisory Yes No Yes Yes Yes No No No No Yes NA1 NA NA NA Yes

T/RH Advisory 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

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


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http://www.tudelft.nl/live/pagina.jsp?id=b66fc20e-24c4-4b89-aaa9-1f8aef51b000&lang=en

http://www.articlesbase.com/self-publishing-articles/agriculture-in-india-issues-and-challenges-203476.html



http://www.jerrydallal.com/LHSP/ranblock.htm

http://www.ikisan.com/Crop%20Specific/Eng/links/ap_groundnutDisease%20Management.shtml

http://www.ikisan.com/Crop%20Specific/Eng/links/ap_groundnutDisease%20Management.shtml

http://www.unce.unr.edu/publications/files/ag/other/fs9841.pdf

http://www.unce.unr.edu/publications/files/ag/other/fs9841.pdf

http://www.scielo.br/pdf/sa/v65nspe/a02v65nsp.pdf

http://www.memsic.com/

Kharif 2009

Generation Standard Week1 Accumulated GDD Egg Stage2 Larval Stage2 Pupal Stage2

I (July 2)

27 59.59 1 22 27

28 190.56 - 13 18

29 316.25 - 5 10

30 446.24 - - 1

31 574.25 - - -

II (Aug 7)

32 41.99 2 25 30

33 165.24 - 16 21

34 281.20 - 8 13

35 388.33 - 1 6

36 468.1 - - -

Rabi 2009

I (Nov 7)

45 13.45 4 36 44

46 117.36 - 27 35

48 309.92 - 8 16

50 469.65 - - -

II (Jan 2)

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 Week refers to one week in a calendar year, beginning as 1 in Jan and ending as 52 in Dec 2 Refers to the number of days remaining for completion of the stage


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