A GSM-based remote wireless automatic monitoring system for field

A GSM-based remote wireless automatic monitoring system for field
c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 2 ( 2 0 0 8 ) 243–259
available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/compag
A GSM-based remote wireless automatic monitoring system
for field information: A case study for ecological monitoring
of the oriental fruit fly, Bactrocera dorsalis (Hendel)
Joe-Air Jiang a , Chwan-Lu Tseng b , Fu-Ming Lu a , En-Cheng Yang c,∗ , Zong-Siou Wu a ,
Chia-Pang Chen a , Shih-Hsiang Lin a , Kuang-Chang Lin b , Chih-Sheng Liao b
a
b
c
Department of Bio-Industrial Mechatronics Engineering, National Taiwan University, Taipei 106, Taiwan
Department of Electrical Engineering, National Taipei University of Technology, Taipei 106, Taiwan
Department of Entomology, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 106, Taiwan
a r t i c l e
i n f o
a b s t r a c t
Article history:
Monitoring field conditions is the foundation of modern agricultural management. In order
Received 1 October 2007
to improve the efficiency of the data collection procedure, and to improve the precision with
Received in revised form
which agricultural operations are managed, it is necessary that we have an automated sys-
7 January 2008
tem that collects environmental data, especially to record long-term and up-to-the-minute
Accepted 8 January 2008
environmental fluctuations. The purpose of this study was to design a remote pest monitoring system based on wireless communication technology. This system automatically reports
Keywords:
environmental conditions and traps pest in real-time. The data we acquired was integrated
Precision agriculture
into a database for census and further analysis. The system consists of two components, a
Environmental parameter
remote monitoring platform (RMP) and a host control platform (HCP). Furthermore, based
monitoring
on the bio-characteristics of the oriental fruit fly, a high precision automated trapping and
Wireless communication
counting device was designed. This device counts the number of trapped flies and then
technology
sends the information back to the RMP. The RMP is in charge of acquiring the environmen-
Mechatronics technology
tal data and the number of trapped flies, and it sends all the data back to the HCP in the form
Bactrocera dorsalis (Hendel)
of a short cell phone message through the wireless Global System of Mobile Communica-
Ecological environment
tion (GSM). Our system then transmits the data via a commercial base station. The system
can work properly based on the effective coverage of base stations, no matter the distance
from RMP to HCP. The function of the HCP is to receive and store, display, and analyze the
database on line. It also provides functions like inquiries, early warning, and announcements. The system was field tested over a 1-year period (March 2006 to July 2007), and the
experimental results demonstrated that it can monitor the environmental parameters and
population dynamics of the oriental fruit fly in real-time. Based on the long-term monitoring database acquired by our system, the relationship between the population dynamics of
the fruit fly and the environmental changes can be easily analyzed. With the help of this
system, researchers can judge the correlation of the occurrence of the oriental fruit fly and
climate conditions. Since the long-term database provides us with the details of the population dynamics of the fruit fly, the system allows us to control the pest in time and reduce
agricultural losses. The experimental results demonstrate that large scale, long distance,
∗
Corresponding author at: Department of Entomology, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 106, Taiwan.
Tel.: +886 2 3366 9640; fax: +886 2 3365 2092.
E-mail address: [email protected] (E.-C. Yang).
0168-1699/$ – see front matter © 2008 Elsevier B.V. All rights reserved.
doi:10.1016/j.compag.2008.01.005
244
c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 2 ( 2 0 0 8 ) 243–259
and long-term monitoring for agricultural information can be achieved by using our proposed monitoring system. Much improved spatial resolution and temporal resolution is
obtained compared to traditional methods for monitoring the data of the oriental fruit fly
based on environmental changes.
© 2008 Elsevier B.V. All rights reserved.
1.
Introduction
Integrated pest management (IPM) relies on the accuracy of
the pest population monitoring technique. Without gathering information of population dynamics together with the
related ecological factors it is almost impossible to execute
the appropriate pest control at the right time in the right place.
However, most insect pests are spread across large areas and
across many boundaries. Like other insect pests, with their
strong reproductive ability the oriental fruit fly, Bactrocera dorsalis (Hendel), is one of the top major pests in the Asia-Pacific
region, causing serious fruit damage and agricultural losses
throughout the whole of Taiwan, year after year. In this article,
we report for the first time a Global System of Mobile Communication (GSM)-based remote wireless automated system for
monitoring the population dynamics of the oriental fruit fly
by means of modern wireless communication technology. By
integrating the traditional trapping method with modern communication technology, our system is able to provide real-time
information on the field conditions and the dynamics of the
pest population at different monitoring sites. This makes the
system especially helpful for large scale monitoring in mountainous areas, which is a major obstacle for the traditional
monitoring methods.
1.1.
Agricultural ecology and pest management
Ecological factors in the environment can be classified as physical (e.g. temperature and humidity), chemical (e.g. chemical
composition of the soil), and biological factors (e.g. pathogens
and pests). As far as cultivation and management of the land is
concerned, the ecological factors are crucial to the quality and
productivity of the crop. Among these factors, pests are those
insects that directly damage the crop, and pest control has
always been considered the most difficult challenge to overcome. To reduce the loss of agricultural products caused by
insect pests, thousands of methods, including physical and
chemical ones, have been developed over the years. Each of
these methods needs to be applied in the right place and the
right time, and many ecological models have been developed
specifically for this purpose. However, without accurate field
information, no theoretical estimations or strategies for pest
control will be able to hit the target and reduce the enormous
costs to both agriculture and ecology.
Since all ecological factors have a dynamic connection with
time, and each of these factors may interact with others, traditional monitoring techniques which rely on manpower to
collect data from trap to trap, point to point are no longer
efficient enough for modern pest management (Shen, 2003).
There are two main drawbacks with traditional pest monitoring: (1) it is labor intensive and therefore costly and (2)
all monitoring points cannot be synchronized to measure the
variables, not even between two recording instruments at a
single site. Given that the traditional monitoring techniques
are labor intensive with a poor temporal property, the dynamics of pest population density in the field cannot be accurately
monitored. Consequently, a proper estimation for a target pest
population will be limited to a long-term scale, such as pest
numbers per 10 days or per 30 days. An example is, the oriental fruit fly (B. dorsalis (Hendel)), which is one of the major
pests of many fruits and many commercial crops in the Asiapacific region. The methyl eugenol trapping method has been
used for more than 30 years, yet we still know very little about
the dispersion of the insect in the field. The oriental fruit fly is
highly fecund, 8–9 generations per year, and is active throughout the entire year in the tropical and sub-tropical countries
(TACTRI/COA, 2006). Previous studies have indicated that the
oriental fruit flies attack 173 different fruits and vegetables
(Metcalf and Metcalf, 1992), and several of them are economically important. By stinging the fruit for oviposition, the fly
infests the fruit in the early stage of fruit development. The larvae hatching inside the fruit causes the fruit to rot, or to make
the fruit ripen and drop before maturation (On News Report,
2004; Lin et al., 2006). Since the population growth and activity
of the oriental fruit fly can be influenced by many ecological
parameters, e.g. temperature, rainfall, wind speed and diurnal
rhythm (TAPHIQ/COA, 2006), an automated system for accurately integrating real-time monitoring information is needed
to better know the related parameters and the dynamic population in different loci.
1.2.
Research purpose
The rapid improvement of today’s micro-fabrication technology and embedded systems allows for a tiny electronic sensor
to integrate multiple functions, like precise sensation and calculation (Gschwind et al., 2001; Ali et al., 2004; Joshua et al.,
2005). By integrating the latest in sensory chips and microfabrication technology with traditional sensor modules and
the pest trapping device, an automated pest and environment
monitoring system can be established, together with powerful processing ability. Furthermore, it will reduce the labor
cost of collecting environmental data by combining the automated monitoring system with wireless telecommunication
technology. By having real-time environmental data available
to us we can better understand the variation among cropland
areas, increase the effectiveness and the precision of cultivation management and establish a highly reliable pest forecast
system. As a result, the automated, wireless pest monitoring system can serve as the major information source for our
agriculture development and insure our competitiveness.
Given that GSM wireless communication has been well
established over all of Taiwan, both in the low-lying areas
and the mountains (He, 2003), transporting data and signals
using the GSM framework will be much more economical than
any other communication technique, especially in the rural
c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 2 ( 2 0 0 8 ) 243–259
areas (He, 2003; Jiang et al., 2003). Tseng et al. (2006) demonstrated the practicability of transporting data by mechatronics
and Global System of Mobile Communication and Short Message Service (GSM–SMS) technologies (ETSI, 1999). Integrating
the sensor modules and GSM wireless communication system
will extend the transmission of the recorded environmental data from hundreds of kilometers away. In addition, an
integrated system will facilitate the accumulation of longterm data and determination of the relationship between
these environmental variations and pest occurrences, and
hence may be the critical reference source needed for effective pest management. The trend of modern agriculture is to
introduce, sensor techniques, long-distance wireless communication (like GSM–SMS), or global positioning systems (GPSs)
and to proceed with automation, precision, and IT. Therefore,
the main purpose of this study is to construct a wireless and
automatic monitoring system that will be useful for monitoring pest population dynamics and related environmental
variations.
1.3.
Research background
1.3.1.
Pest monitoring methods
Many devices have been developed for trapping specific insect
pests, based on the biological characteristics of the pests
(Yang, 1988; Reynolds and Riley, 2002). Today’s research in
the methodology of field surveys for various pests also uses
modern technologies, such as radar technologies for monitoring pest migration or flight (Chapman et al., 2002; Riley
and Smith, 2002), video equipment to observe flying insects
in the field (Riley, 1993), thermal infrared imaging for the
monitoring of rainfall in relation to the control of migrant
pests (Milford and Dugdale, 1990), chemiluminescent tags
for tracking insect movement in darkness (Spencer et al.,
1997), electric systems for detecting moths (Hendricks, 1989),
the micro-bar-code system for monitoring honeybee behavior
(Sasaki, 1989), the remote sensing technique used to detect
the effects of insects on their host plants (usually damage to
crops or forests) or to monitor environmental factors (Riley,
1989; Hay et al., 1997), GPS for wildlife telemetry and habitat mapping (Hurlbert and French, 2001), the high-frequency
echo-sounding method to detect the movements of larvae
(Eckmann, 1998). However, the financial burden for building
the fundamental hardware of these high-tech facilities may
only be affordable to governments or very large agricultural
corporations. At the same time, real-time communication
between data collection and control terminals may not be
easy to achieve since the automatic data may be collected
in remote areas and the communication coverage may be
restricted. Therefore, constructing a monitoring system based
on an automatic and real-time communication platform is
urgently needed for modern agriculture.
1.3.2.
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Chu, 1986). The history of fighting the oriental fruit fly in Taiwan can be traced back for 50 years. In 1955, the poisonous
protein hydrolysate was airdropped in the fields to attract the
fly; in 1975, sterile males which were exposed to 60 Co irradiation were released, coupled with poisonous methyl eugenol as
an integrated pest management (Liu, 1981). In addition, several other methods have been used to control the oriental fruit
fly since then, e.g. chemical control, yellow sticky paper trapping, fruit bagging, etc. (Chu and Chu, 1987; Chiu, 1990; Wu
and Chu, 1990; Chang, 1994; Liu and Hwang, 2000; Chen et
al., 2002; Liu, 2002). Ho (2003) reported that traps containing
methyl eugenol as attractant can trap the most male oriental fruit flies. Therefore, in the present study we use the male
oriental fruit fly as the subject of auto-monitoring and methyl
eugenol as the male-specific attractant.
Traditionally, the population dynamics of the oriental fruit
fly in Taiwan is monitored by yellow plastic traps containing methyl eugenol. The distributing, hanging, and collecting
of the traps in the field are labor intensive and the trapped
amount of the oriental fruit fly is then counted manually,
which makes it even more costly and time consuming. The
survey results of the population dynamics in Taiwan are
therefore measured on a 10-day scale. It is evident that the
traditional method is not economical, nor very efficient considering the limited resolution in space and time scales, and
the lack of environmental parameters. Even though the detection, attraction, and biological characteristics of the oriental
fruit fly are very well known (Liu, 2002; Chen et al., 2002),
nevertheless we still lack a precise pest detection mechanism.
The wireless auto-monitoring system can readily cope
with this problem. Our previous investigation on the automonitoring of the Diamondback moth, Plutella xylostella, one
of the major pests of Brassicaceae (Cruciferae) convincingly
demonstrated the application of an automatic sensory system on pest control (Jiang et al., 2003; Lu et al., 2004; Tseng
et al., 2004, 2006). The moth is attracted by a synthetic sex
pheromone, detected and counted with sensors made of electric grid. In this study, with this experience in mind, we
propose an automated and wireless system which simultaneously records both the environmental variations and the
pest population dynamics. This technology reduces the costs
of both labor and resources and at the same time monitors
the population dynamics with an adjustable temporal resolution in a large space scale. In addition to the collecting of
field data, the developed system is also able to communicate,
integrate, and analysis data in real-time. All the information is received and processed at the server end, and thus a
pest forecast system is constructed with temporal and spatial
precision.
2.
Materials for experiment and system
mapping
Control history of the oriental fruit fly in Taiwan
The subject insect pest of this research is the oriental fruit
fly, B. dorsalis (Hendel) (Diptera: Tephritidae). It is the major
pest for various fruits in Taiwan and is also the target insect
of a quarantine law. The females lay their eggs into fruits
by punching their ovipositor through the fruit’s skin, thereby
greatly reducing the quality of the fruit (Liu, 1981; Chiu and
The software and hardware used to develop our proposed
GSM-based remote wireless automatic monitoring system are
listed as follows:
Software: MSP GCC, PHP, LabVIEW, MySQL, Apache server.
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c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 2 ( 2 0 0 8 ) 243–259
Fig. 1 – Structural diagram of our proposed remote wireless automatic monitoring system.
Fig. 2 – The information flow of our proposed remote wireless automatic monitoring system.
Hardware: sensors for measuring wind speed, temperature,
and humidity, microcontroller (TI MSP430F449 chip), GSM
module, GPS receiver, PC.
The basic make-up of our system can be divided into
two major parts: the Remote Monitoring Platform (RMP) and
the Host Control Platform (HCP) for monitoring and statistical analysis of field information, respectively. The RMPs
are located at measuring places, and each RMP is equipped
with an anemograph, temperature and humidity sensors, GPS
receiver, and GSM module to measure the environmental variants, as well as a pest trap to determine the number of the
insect pest locus in quo. The commercial GSM network system
is adopted for data transmission to the HCP in short message
(SM) format. The GSM module enables the HCP to receive and
transmit field data to a PC for further analysis. The format
design for short message packets used in this study follows
our previous work (Tseng et al., 2006). The graphics user interfaces (GUIs) of the HCP are programmed and integrated on a
Laboratory Virtual Instrument Engineering Workbench (LabVIEW) development platform (Bitter et al., 2001; Ritter, 2002).
The HCP receives field data from the RMPs and stores the
information in the database designed by MySQL (Kofler, 2001).
Via the Worldwide Internet, the user can then explore the
environmental variants and the collected insect pest numbers from the homepage of the RMPs which is programmed
in PHP (Brown, 2002) and stored in the Apache (Melanie, 2001)
server.
2.1.
The structure of the RMP
The structure of the RMP is described as follows: the
MSP430F449 microcontroller developed by Texas Instruments,
Inc. (MSP430F4xx Manual, Texas Instruments, Inc., 2006) is
adopted as the core-processing chip of the RMP. This chip
serves to create data packets, transmit, and dispatch control
commands among the modules used in the RMP. Based on
mechatronics technology, the RMP is designed in a modular
fashion. In addition, the RMP also includes an anemograph,
temperature and humidity meters, a pest-detecting trap, a
GPS receiver, and a GPS module. The anemograph used here
Fig. 3 – System architecture of the remote wireless
monitoring platform.
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is the type of AM-4203 with an accuracy of ±2.0% made by
the Lutron Company. The AM-4203 possesses an RS-232 serial
port and provides 16 bits string output function such that
it can communicate easily with the MSP chip. The sensor
used in the present study for both temperature and humidity measurements is a Sensirion SHT75, which has a built-in
microcontroller to measure the temperature and the relative humidity simultaneously. The SHT75 provides long-term
stability and high measurement accuracies of ±2.0% for relative humidity and ±0.4 ◦ C for temperature. The GSM module
(model no.: FASTRACK M1203A) created by WAVECOM Corpo-
247
ration is used in both the RMP and the HCP. The GSM module
meets both GSM900 and GSM1800 specifications, and has the
same basic capability with the cell phone. The GPS receiver
(GM44, San Jose Navigation Corporation) with an RS-232 interface is used in our system. The GPS receiver characterizes a
15 m positioning accuracy, and mainly provides the geographic
information and correct time tag for the RMP.
Using serial communication ports, the MSP microcontroller
collects each module’s data including environmental information, number of pests, and the RMPs geographical location.
The monitoring data is then packed and becomes a short mes-
Fig. 4 – Construction of remote monitoring platform and finished product: (a) circuit layout, (b) PCB, (c) assembly of RMP
components, and (d) finished product.
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c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 2 ( 2 0 0 8 ) 243–259
Fig. 4 – (Continued ).
sage which is transmitted by GSM module to HCP. As a result,
the system is capable of collecting and transmitting the field
information automatically.
2.2.
or the statistical data can be graphically displayed. This is
particularly useful for future work to establish an identification model for the quantity growth of the oriental fruit
fly.
The structure of the HCP
In addition to the data transmission of the GSM module, the
HCP contains GUIs written in LabVIEW (National Instruments,
Inc.) to control the data transmission from the GSM module (Bitter et al., 2001; Ritter, 2002). The GUIs are linked to
a MySQL database (Kofler, 2001) and can save the field data
received from the RMP into the database of the HCP. The
HCP also contains a dynamic homepage so that the users can
explore the field data collected from the field in real-time as
well as the statistical analysis of historical records via the
Internet. The server adopted in the HCP was established by
Apache (Melanie, 2001), PHP (Brown, 2002), and MySQL. Thus,
we can perform remote monitoring and collect the number of
pests and the environmental parameters, and then save this
field data in the MySQL database. The user and the superintendent of the system can quickly search data from the
PHP dynamic homepage and each item of field information
2.3.
System integration and implementation
2.3.1.
System architecture
Combining the GSM network with the technologies of the
Internet, mechatronics, and wireless communication, this
study constructs a wireless automatic pest monitoring system. The structural diagram of the system is shown in Fig. 1.
The system can be divided into two major parts: the RMP and
the HCP. The RMP uses MSP430F449 as the core-processing
chip and the GPS module for positioning information. The data
recorded by sensors, including the number of trapped flies,
is transferred to the HCP through the commercial GSM network. The user will be able to monitor the field information via
the Internet. Users can obtain the historical records from the
database of the HCP if necessary. Fig. 2 shows the information
flows of the designed remote wireless automatic monitoring
system.
c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 2 ( 2 0 0 8 ) 243–259
2.3.2.
Implementation of the RMP
The system architecture of the RMP is shown in Fig. 3.
The MSP430F449 is the core-processing chip of the RMP for
integrating the GPS and the GSM modules, as well as the
environmental information sensory modules. The environmental information, including temperature, humidity and
wind speed, and the number of trapped flies are the parameters for measurement. Several serial ports are reserved for
future applications. A liquid crystal display (LCD) is also
installed in RMP. Via LCD, users can monitor the collected
information in locus in quo, which is particularly useful for
maintaining the system. Fig. 4 shows the construction of the
RMP. Fig. 4(a) is the circuit layout which is designed with software Protel 99SE. Fig. 4(b) shows the printed circuit board,
including MSP430F449 receptacle, the temperature/humidity
sensory circuit, the circuits for USART voltage level conversion and channel switching, and several output ports which
can connect with the anemometer, the LCD, the GPS and
GSM modules. In addition, it also provides a voltage output to
supply the electrical power for other circuits. The completed
assembly of components is shown in Fig. 4(c). The photograph
of the finished RMP is shown in Fig. 4(d). The outer covering
of the RMP is made of acrylic plates, and the electric circuits
of the RMP are fixed in the box. The whole box is set on an
angle-iron support. The GPS module is the white disc on the
top right corner of the RMP. The anemograph and humidity/temperature sensor are mounted at the bottom of the box
away from the ground.
2.3.3.
Implementation of the HCP
The HCP is set up in a personal computer (PC). The function
flow of the HCP in this monitoring system is shown in Fig. 5.
After receiving the monitoring data by GSM module, the HCP
analyzes the received data and the information regarding the
RMP operational status. If there is an error, the HCP immediately sends a request command for re-transmission, and the
RMP will send the data again. If the trapped pest number is
over the pre-set threshold, the HCP will alert authorized per-
249
sonnel. The main program and the associated GUIs are based
on the planned functions and are developed using LabVIEW.
At the same time, a PHP-programmed dynamic homepage is
developed so that users can retrieve the historical monitoring
data via the Internet. The homepage is very user-friendly.
2.3.4.
GUIs design of the monitoring system
According to the system operation requirements, the designed
GUIs are described as follows.
2.3.4.1. Login page. When the program is activated, the login
page appears. The user is then requested to enter his/her
account and password. If the account and password are correct, the user is then allowed to start the HCP to execute the
setting function.
2.3.4.2. Setting and testing page. Fig. 6 shows the setting and
testing window of the monitoring system. In this page the user
can set up the connection port of the man–machine interface
to the GSM module of the HCP. After choosing the proper connection port, the system will automatically test the connecting
status of the GSM module to the HCP. If the GSM module works,
then the indicator for the module connecting status will be
turned on. Also, the power strength of the received signal is
shown to tell the user the connecting condition of the GSM
module. If the GSM signal is weak, then a weak indication will
remind the user to adjust the antenna for a stronger signal or
change the HCPs location.
After the GSM module is connected, the user starts to set
the RMPs. After choosing the RMP number and entering its
phone number, one can click the “set” button followed by the
“start” button; the system is then automatically activated. By
clicking the “start” button, the HCP will send a ready performance file (RPF) as the starting message to the RMP for the
initial handshake. As the HCP receives the acknowledgement
confirmed by the selected RMP, the start indicator of the RMP
will be turned on to inform the user that the RMP is ready
to use. An instant information frame is located below this
Fig. 5 – Function flow diagram of the host control platform.
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c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 2 ( 2 0 0 8 ) 243–259
Fig. 6 – The setting and testing window for the GSM
module and the RMP.
window, and will list all the information about any action
performed by the HCP and its relevant time.
2.3.4.3. Control interface window. After setting the RMP, clicking the “control interface” button (Fig. 6) the user can enter the
control interface window as shown in Fig. 7(a). On the upper
left side of the window are the indicators of the system status. There are three kinds of system-status indication, start
regular transmission (Tx), start broadcasting, and fault alarming. When the HCP receives an error message sent from the
RMP, the fault alarm is turned on to inform the user, and the
system will send an error message to the maintenance staff.
When the maintenance is finished, one can press the “fault
reset” button on the window and turn off the fault alarm. In
the middle above the window shown in Fig. 7(a) is the latest information in an SM transferred back from the field. It
contains the RMP information, including the RMP number, the
location coordinates of the RMP, temperature, humidity, wind
speed, pest number, and time of data transmitted back. All
the field information will be saved in the database for further
inquiry and analysis. The partial portion of the VI file for performing the “save data” function is shown in Fig. 7(b). Located
in the center of the window is the real-time command line for
requesting the RMP to send the current information immediately or to set the counter to zero. Once the expected data was
lost, the fault alarming module is triggered and the supervisor will be noticed to request the RMP to retransmit the data
promptly or inform the worker locus in quo to fix the malfunction of the RMP. Any information about the executed action
is displayed on the message board in the lower part of the
window (Fig. 7(a)).
Fig. 7 – The control interface window for the operating RMP
at the HCP: (a) GUI and (b) partial portion of VI file for setting
the “broadcast” function in the control interface window.
particularly useful for long-term monitoring. The broadcast
setting function includes the information broadcast, the fault
alarm broadcast, and pest alarm broadcast. The portion of
the VI file for setting the “broadcast” function is shown in
Fig. 8(b). The information broadcast can send the collected
field information to the user after receiving the sensing data,
and thus the user can keep informed and does not need to
stay in front of the computer monitor. If a RMP fault occurs,
the fault alarm broadcast will automatically notify the maintenance staff. In addition, when the number of trapped pests
is larger than the threshold, which is pre-set by the user, information will be sent to the user by the pest alarm broadcast
to reduce the possible pest damage. From the perspective of
agricultural applications, Tseng et al. (2006) reported that the
system, which uses GSM–SMS to transmit data, can ensure
that the remote data arrives on the user’s screen in 30–60 s
without any error found in the data content. Therefore,
the GSM–SMS technology used in this study is feasible and
reliable.
2.4.
2.3.4.4. Advanced
setting
Trapping facility for the oriental fruit fly
window. Fig.
8(a) shows the
advanced setting window which contains the regular transmission (Tx) setting and the broadcast setting functions. The
function of the regular transmission setting allows users
to set the time interval between transmissions and keep
the RMP automatically transmitting data. This function is
Traditionally a mixture of chemical attractant and insecticide
is being used to attract and kill the fruit fly for long-term monitoring of its distribution. The attractant placed in the tube is
for attracting male flies (Steiner, 1952). The trapping device
we adopted in our proposed system is a modification of the
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251
Fig. 8 – Advanced setting window for operating the RMP at the HCP: (a) GUI and (b) partial portion of VI file for setting the
“broadcast” function in the advanced setting window.
traditional fruit fly trapping tube, with an automatic counting
module placed in the tube.
The trapping device in our system uses a double-counting
mechanism (Lin et al., 2006). The number of trapped flies is
counted as they cross the infrared interruption sensor. In general, this kind of counter needs a gate or an inhaler to avoid
counting the same fly more than once. To reduce the complexity as well as the cost of the device, and at the same time obtain
a correct count, a double-counting solution was designed. This
system consists of a set of optical sensors installed along the
trap pathway the fly will pass through. Fig. 9 shows the doublecounting optical sensors which are indicated by the arrows.
The correct counting can be obtained by a microprocessor
which takes the signal interruption into account only when
the fly crosses both optical sensors sequentially. The fly which
hovers around is then excluded by the double counting mechanism. From our observations we found that there are four
possible actions for the fly crossing the double-counting optical sensors, thus we set our counting rules based on these
conditions:
1. If a fly enters the trap and hovers around the first sensor or
even stays on it, the processor then holds the first sensor
in interrupting status until the fly crawls across the second
sensor.
2. If the fly crosses the first sensor and has not yet arrived at
the second sensor, the processor then holds the first sensor
in interrupting status until the second sensor is interrupted.
3. When the fly arrives at the second sensor and both sensors are in interrupting status, the processor will increase
the count and reset the status of both sensors.
Fig. 9 – The double-counting optical sensors deployed
along the pathway.
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c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 2 ( 2 0 0 8 ) 243–259
Fig. 11 – Test for GPS positioning accuracy of the GPS
receiver of the RMP. The test experiments were conducted
at the top balcony of the BIME building, NTU between 19
April 2005 and 26 April 2005.
Fig. 10 – Flowchart of double-counting mechanism for the
designed fly trapping tube.
4. If the fly hovers around the second sensor, the processor
only increases the count once and clears the redundant
signal caused by the hovering. Because the first sensor is
not in the interrupting status, the processor will ignore the
second sensor’s signal even it is interrupted continuously.
This will avoid the fly from crawling back to interrupt the
first sensor again.
Based on the above-mentioned fly behavior, Fig. 10 shows a
flow chart of the double-counting mechanism. Only the occurrence of two consecutive interruptions triggers the counter to
add one. By using this mechanism, compared to the singlecounting method, the double-counting optical sensory set can
be more accurate in counting the trapped flies. Note that in
order to reduce the device complexity, this study ignores the
following conditions: (1) the fly crawls back to the entrance
of the pathway passing through the optical interruption sensors 1 and 2 and (2) the distance between two flies entering
the pathway is less than 2 cm. In this paper, the pathway is
designed to reduce the occurrence of these conditions.
cultivated land, we analyzed the longitude and latitude information collected by the RMP. The RMP was mounted at a fixed
location on the top balcony of the building of the Department
of Bio-Mechanical Engineering (BIME), National Taiwan University (NTU), as shown in Fig. 11. There was no canopy over
the top balcony of the BIME building and no other surrounding buildings to affect the reception of the satellite signal. The
GPS receiver of the RMP received the longitudinal and latitudinal data from the GPS signal every 30 min and delivered it to
the HCP. The system collected 522 data during this operation
lasted about 10 days, and the distribution of the data is shown
in Fig. 12. The red point in Fig. 12 indicates the average of
all data points, which represents north latitude 25.011007◦ , or
equally 25◦ 0 39.63 , and east longitude 121.325819◦ or equally
121◦ 19 32.95 .
During test, the RMP with its single GPS receiver was
located in the fixed location mentioned above. Due to the fact
3.
Experimental results of system
performance evaluation
3.1.
Testing the GPS positioning accuracy
According to the GM-44 manual of the GPS receiver (On Web
of GM-44 Manual, 2006), its positioning accuracy is 15 m. To
confirm if the actual positioning accuracy of a GPS receiver
can provide accurate geographic information for monitoring
Fig. 12 – The distribution diagram of the received longitude
and latitude data for the positioning accuracy test of the
GPS receiver of the RMP between 19 April 2005 and 26 April
2005.
c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 2 ( 2 0 0 8 ) 243–259
253
Fig. 13 – Distribution diagram of the positioning error
margin for the received GPS signals.
that there is no information available on the exact longitude
and latitude of the location of the RMP, we could only take the
average longitude and latitude data as a reference coordinate
for judging the positioning error margin of the GPS receiver.
Comparing every received longitude/latitude data with the
mentioned reference coordinate, the average positioning error
margin was 3.01 m, the maximum error was 22.22 m, the minimum error was 0.13 m, and the standard positioning error was
2.61 m, respectively. The distribution diagram of the positioning error margin of the GPS receiver is shown in Fig. 13. About
90% of the GPS received signals providing positioning data had
a margin of error that fell within 5 m in longitude and latitude compared with the average data. This implies that the
positioning accuracy of the adopted GPS receiver was quite
reliable. Within the scope of this margin of error, if we treat the
average longitude and latitude values as definite ones for a single RMP, it is acceptable. But, if we consider monitoring many
points, we should use a more sophisticated GPS technique or
incorporate the present technique with an electronic map to
fix the RMPs’ position exactly. Then we can effectively build
up a distribution diagram for monitoring the field information
over a wide area.
3.2.
tube
Testing the counting accuracy of the fly trapping
The purpose of this experiment was to determine the counting
accuracy of the fly trapping tube we designed. The experiment was done outdoors, and we compared the number of
flies counted by the system and also counted them manually.
Fig. 14 shows the OrCAD layout and a photograph of the actual
finished product of the flattened pathway of the fly trapping
tube applied in this paper. The cross-section of the entrance is
made larger in order to distribute the attractant and to permit
the flies to enter the trapping tube easily. The height of the
back pathway is lowered to increase the effect of the signal
interruption when a fly passes by, thereby further increasing
the counting accuracy.
The finished product with the double-counting device
installed in the fly trapping tube is shown in Fig. 15. The oriental fruit fly is lured by methyl eugenol and crawls into the
pathway via the entrance and passes by the double-counting
optical sensors mechanism, resulting in the fly being automat-
Fig. 14 – The cross-section of the fly entering pathway in
the designed fly trapping tube: (a) OrCAD layout and (b)
entity photograph of finished product.
ically detected and counted. Fig. 16 shows the enlarged picture
of the RMP equipped with the fly trapping tube. To prevent sunlight influencing the counting accuracy, the upper half of the
fly trapping tube was coated with a dark paint.
Fly trapping tubes equipped with electronic counting circuits were placed under the trees outside of the BIME building
on NTU campus in May 2006. The experimental results are
summarized in Table 1. The average counting accuracy of the
fly trapping tube with two flattened pathways was around
78.1% in this test. From the experimental results, the counting
accuracy of the 6th experiment was only 56.5%. After careful
examination, we found that when the oriental fruit flies crawl
over different pathways, some flies might hide in the corner
inside pathway or retrace the pathway towards the entrance.
This fact also causes the variation in the counting accuracy.
To cope with this problem, we refined the dimension of the
designed pathway and tuned the alignment of the optical sensors. The optimal dimension of the pest-entering pathway is
as shown in Fig. 14(a). The average accuracy is 80.2% without the 6th experiment since the alignment of optical sensors
was missed during the maintenance work. The accuracy is
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c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 2 ( 2 0 0 8 ) 243–259
Table 1 – The experimental results for the counting accuracy of the fly-trapping tube equipped with two flattened
pathways
Evaluation items
Counted number
Actual number
Accuracy (%)
Experimental course
Total
Test 1
Test 2
Test 3
Test 4
Test 5
Test 6
Test 7
11
12
91.7
53
74
71.6
30
35
85.7
18
25
72.0
35
43
81.4
13
23
56.5
36
39
92.3
Average accuracy (%)
196
251
78.1
further confirmed in accordance with the long-term experimental results of the entire monitoring system described in
the next section. With the features mentioned above, the automatic detecting device in this study can attract male oriental
fruit flies and effectively count their number.
3.3.
Field test
Originally two prototypes of RMPs were produced and set up
in the farm on the NTU campus to test their performance,
as shown in Fig. 17(a). Later on, to ensure that the numbers
of trapped flies and the monitored data collected by the system were matched, the two RMPs were moved to outside the
department building of the BIME for outdoor testing as shown
in Fig. 17(b). The HCP of the monitoring system was placed
in our Lab on the 3rd floor of the BIME building, and the distances between the HCP and two RMPs were about 20 and 30 m,
respectively. Since the experimental location was in a bosket
on the NTU campus, the quantity of oriental fruit flies was
smaller than in the farm field. For monitoring field information
and trapping oriental flies, however, this fact did not actually
affect the performance evaluation of our system. The RMP system includes the following items: the core-processing system
integrated by the MSP430F449 chip, the temperature/humidity
sensing module, the GPS receiver module, the GSM module,
Fig. 16 – Our proposed automatic counting trap for the
oriental fruit fly installed in the RMP.
Fig. 15 – The photograph of the double-counting device
installed in the fly trapping tube.
and the anemometer. The fly trapping tube, the anemometer, and the temperature/humidity sensing module were put
in the bottom of the box so as to be shaded and so decelerate
any possible damage of these modules due to severe environmental variation. This arrangement of the sensing modules
can effectively measure the variation of the environmental
parameters.
The whole system has been tested outdoors as of 1st March
2006. At the beginning, the number of flies counted by the fly
trapping tube did not match the number we counted manually, so we changed the standard fly trapping tube with our
improved fly trapping tube in May 2006. After that, the number
counted by fly trapping tube became more accurate and reliable. The temperature, humidity, wind speed, and the number
of trapped flies were then recorded, and the information was
statistically analyzed in the HCP. Fig. 18 shows the monitoring data for a single day as collected by RMP #2 on 4 August
2006. The fly trapping tubes were manually emptied, and the
c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 2 ( 2 0 0 8 ) 243–259
Fig. 17 – Deployment test of the developed remote
monitoring system for the oriental fruit fly: (a) pretest of
the RMP conducted at the farm field on the NTU campus
and (b) long-term test of the RMP conducted at the bosket
near the BIME department building, NTU.
255
number of flies trapped in the fly trapping tube was also manually counted at 10 p.m. each day to verify the accuracy. Except
for the days we performed maintained on the instruments,
we have tested and verified the entire system for 304 days,
from 1st July 2006 to 30th April 2007. During these 304 days,
we deleted the data for 90 days because no flies were trapped
due to rain or extreme low temperature or the number of flies
trapped was lower than 5. The latter recordings were excluded
because in such cases the counting accuracy of the trapping fly
tube varied severely, some were very high and some were very
low to the point that no statistical contribution for long-term
performance evaluation could be obtained. Under these circumstances, the mean accuracies of monitoring oriental fruit
flies by the two RMPs (#1 and #2) deployed on the NTU campus
were 84% and 76%, respectively.
Our proposed system provides different time resolutions
for recording field information, so it is possible for users to
set the time interval for sending the data from the RMPs to
the HCP as required. An interval of 30 min was set in the
present study. For example the data monitored by RMP #2 on
4th August 2006 (Fig. 18), was sent to the HCP every 30 min.
The information included the number of trapped oriental fruit
flies, temperature, humidity, and the mean of the wind speed.
The humidity (%RH), temperature (◦ C), wind speed (cm/s), and
the accumulative number of the trapped oriental fruit flies
are depicted by blue bars, red line, green line, and black line,
respectively. Fig. 18 also shows the trends in humidity and
temperature during the recording time. The highest temperature and the lowest humidity were observed at noon. As it
was getting dark, the temperature became and the humidity
increased, as expected. Based on the monitored information,
we could estimate the local daily activity of the oriental fruit
fly.
The HCP system we developed provides various inquiry
functions for field information. After logging in to the PHP
webpage of our monitoring system, users can set the inquiry
functions at their PC. The daily, weekly, and monthly averaged
Fig. 18 – Monitoring data for a single day collected by RMP #2 in 4 August 2006.
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c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 2 ( 2 0 0 8 ) 243–259
Fig. 19 – The average recordings per day from July to August in 2006 monitored by RMP #1.
data to individual/complex field information can be inquired
from our system. For example, as shown in Fig. 19, the average
recordings of every day from July to August in 2006 monitored
by RMP #1 can be displayed. The system can provide us with
long-term observations on both the environmental variations
and the fly population dynamics in a local area by calculating
the information for each day. Thus the effects of environmental variations on the ecological pattern of oriental fruit fly can
be investigated in a good temporal and spatial resolution if
more RMPs are deployed.
In this study we planned and designed the HCPs database of
the monitoring system in detail so that the database can offer
many kinds of statistical analysis functions for the long-term
recordings of ecological information and various inquiry services for complex data. Thus, if we inquire the data per week,
the influence of more long-term environmental changes on
the population dynamics of the oriental fruit fly can be readily observed via our system. When obtaining the weekly data
points, the correlation between the environmental parameters and the population dynamics of the fruit fly can be
observed. For example, in Taiwan the daily temperature rises
from June until September. Taiwan lies in the subtropical
zone in the northwestern Pacific Ocean. In the mentioned 4month period, this area is subject to frequent thunderstorms
Fig. 20 – The average environmental data and the total population of flies per week monitored by RMP #1 on the NTU
campus.
c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 2 ( 2 0 0 8 ) 243–259
257
Fig. 21 – The hourly records averaged from each day by RMP #1 during the period of 1 June 2006 to 31 December 2006.
in the afternoon, as well as the occasional typhoon. Therefore,
the temperature is not evenly distributed in the mentioned
period. As a result, the number of oriental fruit flies trapped is
closely related to the changes in climate. In northern Taiwan,
July–August is the peak period of the oriental fruit fly activity.
However, their activity reduces gradually when the weather
turns cooler after September.
Fig. 20 shows the average environmental data and the total
population of flies per week detected by RMP #1 of our monitoring system on the NTU campus during the period from
June to November, 2006. According to the data of our longterm recordings, it rained nearly every day between 1 June 2006
and 17 June 2006. In addition, there were four typhoons during 1 July 2006 to 12 July 2006 and 26 July 2006 to 17 August
2006. The climate records from the Taiwan Weather Bureau
also confirmed the recordings from our system. Fig. 20 clearly
indicates that the fly population was quite low during June and
the first 2 weeks of July due to rainy weather. Also, the population curve of trapped flies lowered significantly since the
end of July due to a typhoon over North Taiwan. The fly population increased slowly after around the third week of August,
but went down following the temperature after September as
shown in Fig. 20. This experiment demonstrates that a longterm and a good time resolution can be achieved by our system
in the monitoring of the population of the oriental fruit fly in
correlation to the environmental changes.
With the long-term recording and high temporal resolution provided by our system, the population dynamics of the
oriental fruit fly correlated to climatic parameters can be further investigated. Since this system offers a huge database of
long-term recordings from each of monitoring site, the information can be extracted from the database with any time
interval, be it hourly, daily, weekly or monthly. For example,
we can extract the average data of each hour to analyze the
dynamics of the number of trapped flies and its relationship
to the recorded environmental physical parameters within a
day. Fig. 21 shows the information (collected by RMP #1) of the
averaged hourly records from each day for the period of 1 June
2006 to 31 December 2006. It is clearly shows that the oriental
fruit fly’s daily peak activity is between 06:00 and 10:00 a.m.
This result matches the previous hourly physically observations by Jiang (1986), indicating that our system is capable of
providing high temporal resolution for field information using
an economical recording technology.
3.4.
Statistical analysis of the fly counting accuracy
provided by the proposed monitoring system
To demonstrate and confirm the reliability and accuracy of
our system, the data collected from both RMPs (#1 and #2) for
more than 1 year (RMP #1: 20 July 2006 to 31 July 2007; RMP #2:
1 June 2006 to 31 July 2007) was statistically analyzed. Faulty
data due to system maintenance and zero trapped flies were
eliminated, resulting in 222 and 313 data points being used
in this analysis for RMP #1 and RMP #2, respectively. Fig. 22
shows the linear regression of the counting results from RMP
#1 (a) and RMP #2 (b). Each data point in the figures indicates
the numbers being counted manually and automatically with
reference to the abscissa and the ordinate, respectively. The
slope of the regression line indicates the average accuracy
of the fly counting of the RMP, and R2 represents the reliability of the fly counting accuracy. The average accuracy of
RMP #1 is 81.42% and that of RMP #2 is 73.71%, both with
very high reliability, i.e., R2 = 0.951 for RMP #1 and 0.952 for
RMP #2, respectively. While the selected data in this statistical analysis is in a period different from that mentioned in
Section 3.3, the average counting accuracy of RMP #1 is quite
similar to previous one (i.e., 84%). This fact once again demonstrates the stability and the reliability of performance of our
system.
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c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 2 ( 2 0 0 8 ) 243–259
agricultural management. Also, using our system will be very
helpful for monitoring invading species. In addition, when
combined with a biological identification model and/or ecological theories, the ability to collect field data and have access
to the long-term recording database of this system will be one
of the most useful references and tools for integrative pest
management. For example, through the long-term information collected in the field, including temperature, humidity
and wind speed, and the population dynamics of the pest, one
can create a dynamic population model in a particular application area. This is very useful when making decisions on a
pest control strategy—which depends on applying the right
method at the right time in the right place. Therefore, compared to the traditional monitoring methods, our system can
effectively reduce the cost and increase the effectiveness of
pest control.
Fig. 22 – The linear regression analysis of the counting
accuracy resulting from both RMPs: (a) RMP #1 and (b) RMP
#2.
4.
Conclusions
This study developed a complete and automatic monitoring
system for remote field information. The purpose of developing this monitoring system was to achieve the remote wireless
measurements of environmental parameters and population
dynamics of the oriental fruit fly in real-time. The monitoring system is designed in a modular fashion and consists of
two parts, i.e., RMPs and an HCP. The RMP is equipped with a
MSP430F449 core-processing chip. This chip can package the
sensory data of temperature, humidity, wind speed, and the
number of trapped flies into a short message, and then send
that message to the HCP at a pre-set time interval by GSM
module. The HCP then writes the sensory data into the MySQL
database under the control of a program written in LabVIEW.
By using the PHP website development software, users can
search the remote ecological information of the oriental fruit
fly on the Internet. In addition, any researcher or farmer can
search the historical data on the website, and use the website to analyze the data. The environmental variations can be
monitored in real-time by our system. The long-term monitoring accuracies for the oriental fruit fly achieved by the two RMP
we designed in this study, RMPs #1 and #2, were 81.42% and
73.71%, respectively. According to our results, the reliability
of our automatic fly trapping system is about 95%. Moreover,
since the RMPs automatically sent the field information back
to the HCP every 30 min as scheduled, the daily activity model
of the oriental fruit fly was available and the model matched
the previous study, indicating that our proposed system provides reliable data.
The major contribution of this study is that it makes large
scale, long distance, and long-term monitoring for agricultural
information achievable. High spatial and temporal resolutions
for monitoring the data of the oriental fruit flies with respect to
environmental changes can also be achieved. Via our system
one can easily gather field information in real-time, suggesting a possible application for a pest alarm system in the field of
Acknowledgements
This work was supported in part by the Council of Agriculture
of the Executive Yuan, Taiwan, under contracts: 92AS-1.1.6FD-Z1, 93AS-1.1.6-FD-Z1, and 94AS-1.3.6-FD-Z1. The authors
would also like to thank the National Science Council of Taiwan, for their financial supporting under contract no.: NSC
95-2218-E-002-073.
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