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270 Agro-Informatics and Precision Agriculture 2012 (AIPA 2012)

WIRELESS SENSOR NETWORK FOR THE MANAGEMENT OF BACTERIAL BLIGHT OF POMEGRANATE

Prakashgoud Patil1, Umakant Kulkarni2, B.L.Desai3, V.I. Benagi4 and V.B. Naragund4

1,3B.V. Bhoomaraddi College of Engineering and Technology, Hubli–580031, Karnatka 2S.D.M. College of Engineering and Technology, Dharwad, Karnataka

4University of Agricultural Sciences, Dharwad, Karnataka E-mail: [email protected]; [email protected]; [email protected]

ABSTRACT

Wireless Sensor Networks (WSN) have attracted much attention in recent years. The potential applications of WNSs are immense. The technological development in Wireless Sensor Networks made it possible to use in monitoring and control of weather and crop parameter in precision agriculture. Bacterial blight of pomegranate caused by bacterial pathogen Xanthomonas axonopodis pv. punicae is an important weather related disease that can potentially cause heavy losses in production quality and quantity each year and especially serious in climates with rainfall, and high temperature imparting long period of leaf wetness. Agro meteorological variables i.e. Air Temperature, Air Humidity and Leaf Wetness Duration (LWD) are known to be the parameters, which strongly influence the disease development in pomegranate. So, it is believed that the real time information of agro meteorological variables from the pomegranate fields will provide a solid base for agriculture scientist and farmers to predict the disease well in advance and take control measures.

The WSN system developed in this proposed work is for use in precision agriculture applications, where real time data of weather and crop parameters like leaf wetness, environmental temperature, etc. were sensed from the WSN deployed in pomegranate orchard and sent to the base station and stored in the database. The Infection Index computations were carried out based on sample data but for any package of recommended practices as guidelines for entrepreneurs, data for 3–4 seasons is required. The farmers and scientist can assess and forecast the disease based on the value of Infection Index and can take necessary measures to manage the disease.

Keywords: Precision Agriculture (PA), Wireless Sensor Networks, Bacterial Blight of Pomegranate.

1. INTRODUCTION

A Wireless Sensor Network (WSN) consists of spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants and to cooperatively pass their data through the network to a main location. The modern networks are bi-directional, enabling also to control the activity of the sensors. The development of wireless sensor networks was motivated by military applications such as battlefield surveillance; today such networks are used in many industrial and consumer applications, such as industrial process monitoring and control, precision agriculture and health monitoring, and so on.

This article is organized in the following way: In Section 2, we have a brief overview of bacterial blight of pomegranate such as its Symptoms and Biology, Physiological characteristics—Temperature and leaf wetness, Disease epidemiology—Role of Weather Factors and Management and Control; in Section III, aim of our proposed work is described; in section IV, we discuss material and methodology used to carry out the experiments for the proposed work; Section V presents WSN Architecture for Disease Management in Precision Agriculture; In section VI, we make the data analysis and discussions on results, finally, Section VII conclude the paper.

2. BACTERIAL BLIGHT OF POMEGRANATE

Pomegranate (Punica granatum L.) is an ancient fruit, belonging to the smallest botanical family punicaceae, which is the native of Iran. It is regarded as the “Fruit of Paradise”. It is one of the most adaptable subtropical minor fruit crops and its cultivation is increasing very rapidly. In India, it is regarded as a “vital cash crop”. Successful cultivation of pomegranate in recent years has met with different traumas such as pest and diseases.

Bacterial blight is one of the most devastating diseases of pomegranate occurring in major pomegranate growing states of India. Bacterial blight has been observed damaging the pomegranate crop in moderate to severe proportion resulting in enormous losses in the states of Maharashtra, Karnataka and Andhra Pradesh.

Proceedings of AIPA 2012, INDIA

Wireless Sensor Network for the Management of Bacterial Blight of Pomegranate 271

Fig. 1: Bacterial Blight Disease Symptoms on Pomegranate Fruits along with Healthy Red Color Fruit

2.1 Physiological Characteristics from Literature—Temperature and pH Requirement

Hingorani and Mehta (1952) found that, optimum temperature required for the growth of pomegranate bacterium was 30°C and it tolerated up to 40°C as maximum. Minimum temperature required for the growth is about 5–10°C, the thermal death point was 52°C.

Luxuriant growth and toxin production by Xanthomonas axonopodis pv. vignicola, causal organism of leaf blight of cowpea was recorded at a temperature of 30°C and at pH of 7.0. Growth declined considerably at pH values higher and lower than 7.0, being minimum at pH 5.0 and 9.0 (Gour et al., 2000).

Manjula (2002) observed that the pathogen, Xanthomonas axonopodis pv. punicae, grew at wide temperature range of 20 to 40°C and recorded maximum number of colonies, when inoculated plates were incubated at 27°C. No growth of the pathogen was observed at 10°C. Similarly, pH of 7.2 was found optimum with a range between 5.5 to 8.0.

2.2 Disease Epidemiology—Role of Weather Factors

Bacterial blight of pomegranate caused by bacterial pathogen Xanthomonas axonopodis pv. punicae is an important weather related disease that can potentially cause heavy losses in production quality and quantity each year and especially serious in climates with high rainfall and high relative humidity imparting long period of leaf wetness. Bacterial blight of pomegranate affects leaves, twigs and fruits. Infected fruit and twigs are potential sources of primary inoculums. The secondary spread of bacterium is mainly through rain and spray splashes, irrigation water, pruning tools, humans and insect vectors.

The disease initiates through wounds and its spread is influenced by increased day temperature, low humidity and rain. The bacteria overwinter in infected plant leaves, stems and fruits. The first water-soaked lesions develop within 2–3 days and appear as dark red spots. Disease buildup is rapid from July to September. Severity increases during June and July and reaches a maximum in September and October and then declines. Bacterial cells are capable of surviving in soil for >120 days and also survive in fallen leaves during the off-season. High temperatures and low humidity or both favor disease development.

2.3 Management and Control

No effective chemical control is available for this disease. Use of disease free plant material, removal and burning of infected plant parts, cultivation techniques including pruning technique, pruning period and bactericidal sprays containing antibiotics or copper help to some extent. The infection can be controlled by: Adopting clean methods of cultivation, Orchard sanitation, adoption of proper pruning, use of micro and macro nutrients and Spraying/pasting Streptocycline 0.05 per cent + Copper oxychloride 0.2 per cent. (Raju et al.)

3. AIMS AND OBJECTIVES OF THE WORK

The aim of our work is to design and develop wireless sensor network to measure and monitor environmental and crop parameters for precision agriculture application using Wireless Sensor Network. The specific objectives for our proposed work are as follows: 1. To quantify the effect of agro-metrological parameters on development of disease in pomegranate using the

data set obtained from Wireless Sensor networks.


2. To monitor multiple points within the field to provide a granular level of knowledge regarding the various microclimates and zones in the agriculture field to be monitored.

3. To build and maintain data of the varying crop conditions in a single location to reduce chemical usage and maximize resources so that users maintain healthier, higher quality crops.

4. To provide live datasets and make available to the scientist/farmers through internet at anytime and from any place using WWW for predicting the disease and controlling disease and to provide the timely advice to the farmers.

5. To build data set for the purpose of data warehouse and decision support system for future use.

4. MATERIAL AND METHODS

The MEMSIC eKo Pro Series is a wireless agricultural and environmental sensing system for crop monitoring were used in this work. Some of system components used in this project are depicted in Figure 2.

Fig. 2: eKo Node, eKo Gateway and eKo Base Station

The eKo Node integrates MEMSIC’s IRIS processor/radio board and antenna that are powered by rechargeable batteries and a solar cell. An eKo Node is capable of an outdoor radio range up to 2 miles depending on the deployment and the hardware configuration chosen. The nodes themselves form a wireless mesh network that can be used to extend the range of coverage. By simply adding an additional eKo Node, it is easy to expand coverage area. The nodes come pre- programmed and configured with MEMSIC’s XMesh low-power networking protocol. This provides plug-and-play network scalability for wireless sensor networks.

4.1 Sensors for Environment and Crop Parameter Measurements

This section presents the details of the sensor used in this work with their features and specifications.

1. Soil Moisture and Temperature: The ES1100 Watermark Sensor (granular matrix sensor) is a soil moisture and soil temperature sensor. Up to four ES1100 sensors can be connected to one eKo Node to measure soil moisture at different soil depths. By monitoring the sensor measurements between irrigations, it is possible to measure the rate at which the soil is drying out.

2. Soil Water Content: The ES1110 uses the Decagon EC-5 which obtains volumetric water content by measuring the dielectric constant of the media through the utilization of capacitance/frequency domain technology. It incorporates a high frequency oscillation which allows the sensor to accurately measure soil moisture in any soil with minimal salinity and textural effects.


3. Ambient Temperature and Humidity: The ES1201 is a temperature/humidity sensor that measures the ambient relative humidity and air temperature. These readings are also used to calculate dew point. The sensor enclosure protects the sensor from mechanical damage, and a membrane filter protects the sensor elements from dust, dirt, and water spray.

4. Leaf Wetness: Leaf wetness sensor from Decagon. Many fungal and bacterial diseases affect plants only when moisture is present on a leaf surface. The leaf wetness determines the presence and duration of canopy wetness allowing users to forecast disease and protect the plant canopy.

5. Solar Radiation: The ES1401 uses the Davis Solar Radiation sensor and measures global radiation, both the direct and diffuse components of solar irradiance. It allows users to monitor evapotranspiration.

Fig. 3: C3088OV6620 Camera

6. Image Sensor: An image sensor is a device that converts an optical image into an electronic signal. It is used mostly in digital cameras, camera modules and other imaging devices. Early analog sensors were video camera tubes, most currently used are digital charge-coupled device (CCD) or complementary metal–oxide–semiconductor (CMOS) active pixel sensors.

7. C3088 OV6620 Camera with Digital Output for AVR MCU Interfacing: C3088 based camera is the most AVR friendly camera for interfacing applications. It is supported by Atmega8, Atmega16 and ATmega644. Most other camera available in the market has analog output which is very difficult to work in microcontroller like AVR or PIC. This camera is little different in the sense it outputs digital data which can be worked with an AVR MCU (8-and 32-bit Microcontrollers- Atmel Corporation) very easily.

5. WSN ARCHITECTURE FOR MONITORING AGRO METEROLOGICAL DATA

Figure 4 depicts the system architecture for crop disease management for precision agriculture using wireless sensor network. To ensure proper coverage and connectivity four nodes are deployed in the field to measure agro-metrological parameters. Figure 4 shows the deployment of WSN node with interface details. The eKo gateway is utilized as gateway which runs the Ubuntu Linux operating system. It comes preloaded with MEMSIC’s Sensor Network management and data visualization software packages, eKoView and XServe. These programmes are automatically started when the gateway is turned on. Plug-and-play at start-up, the gateway and eKoView web interface easily allows users to view data real-time, run reports, set alerts and more.

Fig. 4: WSN Architecture for Disease Management in Precision Agriculture


To extract the WSN data from the internet the base station is configured and connected to internet using the fixed IP address using broadband services. Thus, it is made possible to login to Web Application which is running in the base station from Internet and extract the live crop and weather data sensed from the WSN at any time and from any place.

In our future enhanced version of our work an image sensor—(C3088 OV6620 Camera) is used to take digital images of Pomegranate plant including fruits, leaves and stems. The C3088 OV6620 Camera produces the digital output. C3088 based camera is the most AVR friendly camera for interfacing applications.

Fig. 5: Deployment of WSN node with Details of Interface

6. DATA ANALYSIS AND RESULTS

This section describes the agricultural experiments conducted in the field, which concentrated on monitoring different parameters relating to crop and climate by deploying the wireless sensors so as to establish a correlation between sensors’ output and agricultural requirement in terms of disease management. The WSN system deployed at greenhouse, at Plant Pathology Department, University of Agriculture Sciences, Dharwad equipped with sensors for continuously monitoring agricultural parameters i.e. air temperature, air relative humidity, soil temperature and leaf wetness. Each node was able to transmit/receive packets to/from other nodes every 30 minutes over a transmission range of 30 m. Data collected by the sensors were wirelessly transferred in multi-hop manner to a base station node (about 700 m away from the mote) connected with embedded gateway for data logging and correlation.

6.1 Effect of Temperature and Leaf Wetness Duration on Infection Index

Two different models i.e. Logistic and Beta models evaluated for estimation of infection index, as function of environment variables have been used which are as given below. The probability of disease development in a defined time period is described by a logistic regression model.

The Logistic model can be written in the form:

f (T, W) = ln (Y/1–Y) where, W = leaf wetness duration in hours; T = ambient temperature in Degree Celsius; and ln (Y/1–Y) = the logistic of disease incidence Y = the proportion of infected berries f (T, W) = linear function consisting of summation of terms involving parameters multiplied by powers of T and W (T, T2, W, W2, WT, WT2 etc)

Infection Index = (–2.64786) – (0.37493W) + (0.0616WT) – (0.00151WT2)

While the Generalized Beta Model (Erincik et al., 2003) can be written in the form

min

max min

(1 )( )

( )

β γ δ= α −−

=−

Y t WT Tt

T T


Tmin = Minimum temperature for infection Tmax = Maximum temperature for infection

α, β, γ, δ are parameters are estimated from experimental data. The parameter β describes how steeply Y increases with increasing T up to optimum; the parameter γ describes how steeply Y decreases as T increases past the optimum. The estimated values of these four parameters were reported by Erincik et al. (2003). The range of values Infection index and Risk Levels are as mentioned in the Table1. The Risk Infection Index based on Temperature and Leaf Wetness Duration are calculated based on Logistic and Beta method and which are as shown in Table 2.

Table 1: Values of Infection Index and Risk of Infection

Infection Index Values Risk Levels 0 or below No risk of infection 0.01 to 0.50 Low risk of infection 0.50 to 1.00 Moderate risk of infection Above 1.00 High risk of infection

Table 2: Risk of Infection using Logistic and Beta Method

Temperature (°C) Leaf Wetness Duration (hrs) Logistic Beta Risk of Infection 21 4.17 – – No risk 21 9.97 0.201 0.128 Low risk 22 13.85 0.882 0.584 Moderate risk 21 12.12 0.878 0.612 Moderate risk

7. CONCLUSION AND FUTURE WORK

This work presents the design and the implementation of a Wireless Sensor Network that monitors the air temperature, humidity and leaf wetness in a crop field. The sensor data is wirelessly transmitted to a centrally located computer terminal that logs the field data within seconds. The data collected can aid the farmers in achieving maximal crop productiveness by managing the disease. WSN test bed facility at University of Agriculture Sciences, Dharwad helped in computing infection index values based on ambient temperature and leaf-wetness duration. The Infection Index computations were carried out based on sample data but for any package of recommended practices as guidelines for entrepreneurs, data for 3–4 seasons is required. However the technical aspects of WSN deployment and use in agriculture are satisfactorily tested and demonstrated in field.

The accuracy of disease forecast can be enhanced by considering the some more environmental and crop parameters like pH value and solar radiation for making better assessment of disease. As future development of the project, it is proposed to make use of image sensor to take the live images of the crop. The images obtained by the camera can be accessed by scientist through web interface which helps to understand the severity of the disease.

ACKNOWLEDGEMENT

The authors would like to thank Dr. Ashok Shettar, Principal, and B.V.B College of Engineering and Tech for his valuable support and motivation for our work.

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presented during 2nd International Symposium on Pomegranate and Minor including Mediterranean fruits held on 23–27th June, 2009 at UAS Dharwad, 53–58.

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Chaudhary, D.D. et al. Application of Wireless Sensor Networks for Greenhouse Parameter Control in Precision Agriculture. Dalla Marta, A., Magarey, R.D. and Orlandini, S., 2005, Modeling Leaf Wetness Duration and Downey Mildew Simulation on

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Hingorani, M.K. and Mehta, P.P., 1952, Bacterial leaf spot of pomegranate. Indian Phytopath, 5: 55–56. http://www.memsic.com/products/wireless-sensor-networks.html http://www.sunrom.com/sensors/camera-imaging/c3088-ov6620-camera-with digital-output-for-avr-mcu-interfacing?sunid=42baf9454cf4381567c390090fb1d3f4

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Raju, J., Benagi, V.I., Nargund, V.B., Sonavane, S. P. and Mesta, R.K., 2011, In vitro evaluation of antibiotics, botanicals and bioagents against bacterial blight of pomegranate caused by Xanthomonas axonopodis pv. punicae. Paper presented in National symposium on “Biology of infection, immunity and disease control in pathogen-plant interactions” held at Department of Plant Sciences, School of Life Sciences, University of Hyderabad, Hyderabad, 2–4 December 2011, p. 177.

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