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1. Early Warning Device for Brown Plant
Hopper Detection in Palay Using Wireless
Sensor Networks
Ma. Lourdes L. Mistal1
, Jocelyn Flores Villaverde1*
and Buyung Hadi2
1
School of Electrical, Electronics, and Computer Engineering
Mapúa University, Manila, Philippines
2
International Rice Research Institute
Laguna, Philippines
*
jfvillaverde@gmail.com
Abstract— Rice (oryza sativa) is one of the most in
demand staple foods in the world because of its
versatility in processing and consumption. But due to
the ever increasing human population, rice farmers
and producers are pressured to produce more from
just one to three cropping seasons. Climatic change
also affects rice production as well as the heavy use of
chemical inputs. These factors contribute to the
problems of pest outbreaks and disease attacks to rice.
Brown planthoppers (BPH, Nilaparvata lugens) are
among the outbreak pests of rice that have brought
heavy losses in several Asian countries including the
Philippines. Its early detection is crucial because this
pest is a virus carrier that can cause tremendous yield
loss to rice. With this, designing an early warning
device is a critical element for BPH pest management
system. Image processing is one of the early detection
mechanisms proposed in the literature. In this
research, an early warning device using Wireless
Sensor Network (WSN) through Haar feature-based
cascade classifiers for BPH detection is being proposed
which can be installed in strategic locations in the
palay-farm-area under study. This device is
programmed to send warning messages when
acceptable range of the BPH cascade classifiers is
obtained; thereby, allowing the end-users to check the
status of the palay farm area. The researcher develops
an early warning device prototype and performs an
extensive set of experiments measuring its image
detection capability.
Keywords— Wireless Sensor Network (WSN),
Brown Plant Hopper (BPH), Image Processing, Short
Message Service (SMS), Pest Detection
I. INTRODUCTION
Rice (Oryza sativa), along with wheat and corn,
is the most widely cultivated crops in the world for
centuries. Due to its demand to feed billions of
rapidly increasing human population, rice farmers
and producers are pressured to resort to intensified
production from just one to three cropping seasons
annually. This factor had worsened the occurrence of
pest and disease attacks in rice because of the
increasing popularity of heavy chemical input. The
pest outbreaks and disease attacks are because of
pesticide resistance and the death of natural rice-pest
enemies that could have provided ecosystem services
to reduce vulnerability to outbreaks. Rice Brown
Planthoppers (BPH) Nilaparvata lugens (Stål) are
among the outbreak pests of rice that has brought
heavy losses in several Asian countries including the
Philippines [1-3].
There were various studies conducted in
response to the possible threat of BPH and other pest
and diseases [4-9]. Early detection systems are
crucial to fight this pest since they are also a virus-
disease-carrier such as the Rice Ragged Stunt and
Rice Grassy Stunt diseases which can immediately
spread in the entire palay area [10]. In [11], the
detection of pest and diseases particularly on the
BPH use hyperspectral remote sensing. It can detect
stress on potted rice plants through reflectance
compared to the uninfected plants. The BPH is
destructive because visible symptoms to palay only
appear when the stage of infestation is severe. With
leaf discoloration, it is too late to save the palay and
preventive actions are effectual. However, the use of
Wireless Sensor Networks (WSNs) [12] with Xbee
for network and microscopic camera as sensor can
easily detect pests through object detection using
computer vision. The study [13] showed a simple yet
efficient way to detect and extract pests in the palay
practice farm in Pampanga. The researchers used
background modelling that determined the presence
of pests and median filter to remove noise that was
produced by different lightning conditions. As of
now, there are no similar studies recorded on the
detection of Brown Planthopper specifically on palay
crop using image analysis and sends alerts to the
farmer of its occurrence. However, the study of
automation in counting of planthoppers based on
image processing can be used but needs further
research and developments for its applicability on
palay.
In recent years, the BPH has caused extensive
damage to rice crop in Asia namely Brunei,
Bangladesh, China, Malaysia, Sri Lanka, Thailand
and Vietnam [14]. But the insect’s large scale
damage has been reported in India, Indonesia, Sri
Lanka and the Philippines. The BPH is regarded by

2. many as the number-one insect pest of rice in Asia
today. Thus, if not detected early, its unpredictable
infestation could result to severe damage in palay and
loss of production for farmers. Also, BPH’s sudden
occurrence may force the farmers to use unnecessary
insecticides which could lead to its outbreak because
of the death of natural enemies [15].
The general objective of the study is to develop
an early warning device that can detect BPH in palay
using Wireless Sensor Networks. Specifically, this
used a microscopic camera to capture video feeds of
the BPH on palay plant using Haar feature-based
cascade classifier and send an SMS message to the
farmer informing of the occurrence of the said pest.
The early warning system would be beneficial to
the farmer because it would reduce the farmer’s time
and effort in monitoring the palay farm physically. It
would also give the farmer sufficient time to plan and
employ appropriate pest control methods such as
natural chemical and biological control, integrated
pest management and other methods which can be
very beneficial to the farmer and most importantly to
the environment.
The study is designed to detect BPH pest of palay
crop at reproductive stage under controlled
environment. This followed the recommended
fertilizer and temperature requirements and the usual
farm practice and care for this crop. The system
captured the video images of the BPH pests using
microscopic camera and not the image of the damage
it caused to the plant. The obtained images were then
programmed into the system through Haar feature-
based cascade classifiers and warning signals were
sent to the farmer through SMS messaging.
Necessary interventions were done by the farmer and
not by the system.
II.METHODOLOGY
A. Evaluation Phase
In the evaluation phase of the research, the
researchers gathered the necessary equipment to be
used in obtaining the images of the BPH. The
International Rice Research Institute (IRRI) in Los
Baños, Laguna provided the needed pests, palay
plant and its container. The insects and palay plant
are kept in a cage made of Mylar plastic film that
encloses the cage and organza cloth that serves as the
door (front) and exhaust (back) of the cage. The
experiment is done using microscopic camera to
capture video and still images of the BPH. It is
further conducted at the insectary of IRRI. Pot
culture rice is used with seeds of Taichung Native 1
(TN1) variety. The age of the palay plant is 50-60
days after transplanting. The BPH is 2-3 day old
adults and reared in covered cages under natural
temperature. All experiments are conducted under
the same natural temperature.
Per container, approximately 200 adult BPH is
placed into the palay plant that is on its reproductive
stage. Reproductive stage refers to the stage from
booting to blooming, flowering, heading, tasseling
and milking stage (46 to 75 days after crop
establishment).
Fig. 1. (a) BPH as the Positive images and (b) Green Leaf
Hopper and Black Bugs as the Negative Images. There were 200
positive and 200 negative sample images that were trained and
created a cascade classifiers based on Haar-liked features.
Object detection using Haar feature-based
cascade classifier is used in the study. This is an
effective object detection method that was proposed
by Paul Viola and Michael Jones in their paper
entitled “Rapid Object Detection using a Boosted
Cascade of Simple Features” [16]. A cascade
function is trained from a number of positive and
negative images. To create cascade classifiers based
on Haar-like features, 200 positive and 200 negative
sample images are trained which were captured using
the microscopic camera as shown in Fig. 1. The
positive images are images that contain the BPH and
the negative images are those that do not contain the
object. Then, the positive images are marked. These
are the rear part (butt) of the BPH. A vector data file
was created based on the positive marked images
which contain the names of positive images and the
location of the objects in each image. The classifier
is then trained and run using an OpenCV file. Haar-
like features shown in Fig. 2 are used. Through
subtracting the sum of pixels that are under the white
rectangle from sum of pixels under the black
rectangle, a single value is obtained from each
feature.
Fig. 2. Examples of Haar-liked Features
There are three sensor nodes and one master
node that are placed in strategic locations within the
area under study. One sensor node is composed of
microscopic camera, Raspberry Pi 3 Model B and
XBee module. The master node is responsible in the
notification of the farmer. The master node is
composed of Arduino, XBee module and GSM
module. Fig. 3 shows the diagram of the early
warning device. The microscopic camera is placed 5
inches away from the palay plant. Mesh is the type of
networking topology that is used in the study. All the
(a)
(b)

3. sensor nodes are designed to communicate with the
master node as well as with each other.
Fig. 3. Diagram of the early warning device. This is
composed of microscopic camera that captures images of the
insects, raspberry pi which acts as the main controller and Xbee
module that acts as the transmitter and receiver of data. The
master node is composed of Xbee module that serves as the
receiver of data, Arduino which controls the XBee module and
the GSM module and SIM800 that sends text to a predefined
mobile number.
B. Development Phase
In the development phase, the researchers
gathered all the necessary materials/equipment used
in the study and set-up the needed hardware. The
sensor nodes are set-up according to the required
operation and structure. The system programs are
developed in this phase and tested if it runs properly.
For Raspberry Pi, the latest version of Raspbian
Operating System (OS) which is Raspbian Stretch is
installed. The application and system files are also
updated. Also, the Python-OpenCV package is
installed. The XBee modules are updated to the latest
firmware version. One (1) of the XBee modules is
set-up as master node and two (2) nodes are set-up as
sensor nodes. These nodes are set to connect to the
master node. For Arduino that will act as master
node, the XBee connectivity is first tested by
connecting it to a PC using XCTU platform
application. Packets of data are sent to an XBee that
is connected to the Arduino microcontroller. The
Arduino is then tested to send text message to a
mobile number using the GSM module (SIM800).
The code to send the said text message is developed
to a predefined mobile number which is assumed to
be the farmer’s number. A text message is sent when
the XBee module receives data from any sensor
node.
The Raspberry Pi as sensor node is developed
with test code using Python OpenCV library with the
generated cascade classifier to detect the BPH from
video feed. Another code is developed to test the
connectivity of sensor node to the master node. For
the sensor node, code is developed to first, sends data
to the master node once BPH from the video feed is
detected and second, allows the receiving of data
from other sensor nodes which is to be sent to the
master node.
C. Implementation Phase
In this phase, all the necessary materials and
equipment are installed. The researchers also set-up
the farm prototype as shown in Fig. 4 with the BPH
pests and integrates it with the program. Test run is
done if the system is correctly running according to
the desired output.
Fig. 4. Final Prototype of the Early Warning Device. This shows
that nodes 1-3 are set-up with the insect, camera, raspberry pi and
Xbee module. The master node is also running. The nodes and
the master node are connected with power supply.
D. Testing Phase
In the testing phase, the system is tested if it
properly detects BPH pest and if it sends text
message to the farmer. Initially, the microscopic
camera is set by calibrating its focus to the sharpest
detail possible. This will give a clear and define
image of the pest. The camera is set 5-inch away
from the plant. Once set, the sensor nodes and master
node are initialized to process the captured data. Only
a single frame that matches the BPH cascade
classifier is needed to trigger the system to send an
SMS to the mobile phone. The message contains
string that says that a pest was detected which also
contains the node number. The system only covers
until the sending of SMS message to farmer. The
interventions are done by the farmer.
III. RESULTS AND DISCUSSION
The researcher performed detection of the BPH
through getting a random sample of 50 BPH and
caged it individually. The system was set-up and the
camera was focused on to the sharpest detail
possible. The researchers also tested the validity of
the system by testing other pests such as 10 Green
Leaf Hoppers (GLH) and 10 black bugs. Fig. 5 shows
the actual detection of the BPH when the system was
run. The system boxed the rear part of the insect
which entailed positive detection of the BPH under
study.

4. Fig. 5. Actual detection of the BPH. This shows that the rear
part of the insect was boxed when the system was run.
The second testing was done to test the sending
of text to the predefined cellular phone number when
the pest is detected from one node. The researchers
started to test all the nodes separately by setting the
camera and sensor node with BPH. Then the
researchers tested all nodes (1) with BPH and (2)
only nodes 1 and 3 have BPH. All text messages
were sent to the cellular number with the correct node
number.
The third testing was done to test the system if it
will send the text message within the two (2) minute
period. Only node 1 was used and set to have the
BPH. As a result, the system correctly sent messages
every two (2) minutes with node 1 as the sensor that
detected the pest.
Table 1 was used to show that the proposed
system can detect captured images of the BPH
through Haar feature-based cascade classifier.
TABLE I. CONFUSION MATRIX FOR TESTING OF BPH
AND NON-BPH
In the confusion matrix, of the 50 actual BPHs,
the system predicted that two were not BPH, and of
the 10 Green Leaf Hoppers and 10 Black Bugs, it
predicted that all were not BPH. Fig. 6 shows the
images that have been tested by the system to get the
percentage accuracy of classifying the BPH
correctly.
Fig. 6. Example of Test Captured Images for (a) BPH, (b) GLH
and (c) Black Bug. This shows that the BPH were correctly
boxed and detected. The GLH and Black Bugs were also
predicted as non-BPH
The accuracy from the confusion matrix table can
be computed using the formula,
Accuracy (ACC) = Σ True positive + Σ True
negative / Σ Total population (1)
Thus, the accuracy of the detection of BPH is,
Accuracy = ((48+20)/70) x 100 = 97.14%
This means that the early warning device is
capable of detecting the BPH for about 97 percent.
From gathering 70 images of pests that were
composed of 50 BPH, 10 GLH and 10 BB, 48 out of
50 images of BPH were detected correctly and 20
out of 20 images of non-BPH (GLH and BB) were
correctly detected by the system. The two (2) BPH
that were not detected was due to some data that was
not trained considering the pest position.
TABLE II. DETECTION OF BPH AND SENDING OF TEXT
MESSAGE
Table 2 shows that the system was able to detect
the pest and sent the text message to the farmer with
an interval of two minutes. For testing purposes, the
researcher set the system to two minutes instead of
12 hours.
IV. CONCLUSION
From this paper, an accurate detection of BPH is
presented, tested and implemented. The prototype
was designed using microscopic camera to
efficiently capture the images of the BPH and
installed in strategic locations within the palay area
under study. This is connected to the Raspberry Pi
which is the main controller of the nodes. It is coded
in Python with OpenCV library that is used for
computer vision. It is also responsible for
transmitting data through XBee module to master
node while the Arduino is the one that controls the
XBee module and the GSM module in the master
node. The information from nodes is passed on to the
nearest node until it reaches the master node.
Through the data received, Arduino formats the text
message string replacing variables with node
number. The researcher gained a result of correctly
sending an SMS to the farmer through the GSM
module when BPH is detected.
Also, the researcher used the image processing
technique such as Haar feature-based cascade
n=70 BPH Non-BPH
BPH TP=48 FP=2 50
Non-BPH FN=0 TN=20 20
48 23 70
Predicted
Actual
Detected? Sent Message?
(Yes or No) (Yes or No)
1 2:24:34 AM Yes Yes
2 2:26:34 AM Yes Yes
3 2:28:31 AM Yes Yes
4 2:30:31 AM Yes Yes
5 2:32:27 AM Yes Yes
6 2:34:27 AM Yes Yes
7 2:36:41 AM Yes Yes
8 2:38:41 AM Yes Yes
9 2:40:27 AM Yes Yes
10 2:42:27 AM Yes Yes
11 2:44:14 AM Yes Yes
12 2:46:14 AM Yes Yes
Test No. Time
(a)
(b) (c)

5. classifier for pre-processing of the images. This
process gave percentage accuracy of 97.14% were
the differences vary from the manner the image is
captured.
Based on the findings and results of the trainings
and testing that have been done, the researcher
recommends the following for the improvement of
the system: for better image quality, it is better to use
camera with higher specification especially on
magnification capability and to include in the system
the ability to determine the number of BPH present
in the area for appropriate intervention to be done by
the farmer.
ACKNOWLEDGMENT
The authors are thankful to the entomologists
under Crop and Environmental Sciences Division in
IRRI, Los Baños, Laguna for providing necessary
facility and materials to undertake this research.
Also, this research has been funded by the
Engineering Research and Development for
Technology under the Department of Science and
Technology (DOST).
REFERENCES
[1] V. A. Dyck, and B. Thomas, “The brown planthopper
problem. In: Brown planthopper: threat to rice production
in Asia,” International Rice Research Institute, Los Baños,
Laguna, Philippines. Pp. 3-17: 1979.
[2] G.R. Wu, X.P. Yu, L.Y. Tao, “Long-term forecast on the
outbreak of brown planthopper (Nilaparvata lugens Stal)
and white-backed planthopper (Sogatel la f urcif era
Horvath). Sci. Agric. Sinica Pp. 30, 26–30: 1997
[3] O. Mochida, and T. Okada, “Taxonomy and biology of
Nilaparvata lugens (Hom: Delphacidae). In: Brown
planthopper: threat to rice production in Asia,” Los Baños
(Philippines): International Rice Research Institute. P 21-
43.
[4] Y. Qing, X. Ding-xiang, L. Qing-jie, Y. Bao-jun, D. Guang-
qiang, and T. Jian, “Automated Counting of Rice
Planthoppers in Paddy Fields Based on Image Processing,”
Journal of Integrative Agriculture, August 2014, 13(8):
1736-1745.
[5] N. Mongkolchart, and M. Ketcham, “The Measurement of
Brown Planthopper by Image Processing,” International
Conference on Advanced Computational Technologies and
Creative Media (ICACTCM’2014).
[6] S. Xu, Z. Zhou, H. Lu, X. Luo, Y. Lan, Y. Zhang, and Y.
Li, “Estimation of the Age and Amount of Brown Rice Plant
Hoppers Based on Bionic Electronic Nose Use,” Sensors,
14, 18114-18130; doi:10.3390/s141018114: 2014
[7] Y. Yang, B. Peng and J. Wang, “A System for Detection
and Recognition of Pests in Stored-Grain Based on Video
Analysis,” vol. 344, D. Li, Y. Liu and Y. Chen, Eds. Berlin,
Heidelberg: 2011.
[8] C. Priya, K. Praveen, and A. Srividya, “Monitoring of Pest
Insect Traps Using Image Sensors & Dspic,” International
Journal of Engineering Trends And Technology, vol 4 Issue
9.
[9] G. Bhadane, S. Sharma, and V. Nerkar, “Early Pest
Identification in Agricultural Crops using Image Processing
Techniques,” International Journal of Electrical, Electronics
and Computer Engineering, 2(2): 7 -82(2013), ISN No.
(Online): 27-2626.
[10] P. Cabauatan, R. Cabunagan, and R. Choi, “Rice viruses
transmitted by the brown planthopper Nilaparvata lugens
Stål,” International Rice Research Institute: 2009
[11] N.R. Prasannakumar, S. Chander, R.N. Sahoo, V.K, Gupta,
“Assessment of Brown Planthopper, (Nilaparvata lugen)
[Stal], damage in rice using hyperspectral remote sensing,”
International Journal of Pest Management, vol. 59, no. 3,
pp180-188, 2013
[12] S. Datir, and S. Wagh, “Monitoring and Detection of
Agriculture Disease Using WSN,” IJCA, vol 87: 0975-
8887:2014.
[13] J. Miranda, B. Gerardo, and B. Tanguilig III, “Pest
Detection and Extraction Using Image Processing
Technique,” International Journal of Computer and
Communication Engineering, vol. 3, no. 3.
[14] J.L.A. Catindig, G.S. Arida, S.E. Baehaki, J.S. Bentur, L.Q.
Cuong, M. Norowi, W. Rattanakarn, W. Sriratanasak, J.
Xia, and Z. Lu, “Situation of planthoppers in Asia,
“International Rice Research Institute, Los Baños, Laguna,
Philippines. Pp. 191-220: 2009.
[15] K.L. Heong, K.H. Tan, C.P.F. Garcia, Z. Liu, and Z. Lu,
“Research Methods in Toxicology and Insecticide
Resistance Monitoring of Rice Planthoppers”, International
Rice Research Institute, Los Baños, Laguna, Philippines.
Pp. 3-17: 2013.
[16] P. Viola, M.J. Jones, “Rapid object detection using a
boosted cascade of simple features,” CVPR ’01:
Proceedings of the Conference on Computer Vision and
Pattern Recognition, Los Alamitos, California, USA. Pp
511-518: 2001.

6. Haar Feature-Based Cascade Classifier for
Nilaparvata Lugens Detection
Ma. Lourdes L. Mistal1
, Jocelyn Flores Villaverde1*
and Buyung Hadi2
1
School of Electrical, Electronics, and Computer Engineering
Mapúa University, Manila, Philippines
2
International Rice Research Institute
Laguna, Philippines
*
jfvillaverde@gmail.com
Abstract—Detection of pest in palay is important
to the farmers for better production. Manual pest
survey methods in palay farms are time-consuming
and tedious. This paper aims to develop a device that
can detect Nilaparvata lugens through an OpenCV-
Python code using Haar feature-based cascade
classifier algorithm. Currently, BPH are among the
outbreak pests of rice that have brought heavy losses
in several Asian countries including the Philippines. Its
early detection is crucial because this pest is a virus
carrier that can cause tremendous yield loss to rice.
With this, designing a detection device is a critical
element for BPH pest management system. Image
processing is one of the early detection mechanisms
proposed in the literature. The Python code was tested
and its output was validated which achieved a 97.14%
accuracy rate.
Keywords—Nilaparvata lugens, Image Processing,
Haar feature-based cascade classifier, Pest Detection
I. INTRODUCTION
The method of converting an image into digital
form is called image processing. This technique is
employed to various fields such as agricultural sector
specifically rice farming to detect pests. Rice Brown
Planthoppers (BPH) or Nilaparvata lugens (Stål) are
among the outbreak pests of rice that has brought
heavy losses in several Asian countries including the
Philippines [1-3].
There were various studies conducted in
response to the possible threat of BPH and other
pests/diseases through inputs such as video or still
images [4-11]. In [4], it uses a handheld device for
detecting and assessing the population density of the
RPH based on image processing. For detection, they
use a digital camera with Wi-Fi, a smart phone and
an extendable pole. While for counting, they adopt
the AdaBoost classifier based on Haar features,
Support Vector
Machine (SVM) classifier based on Histogram of
Oriented Gradient (HOG) features and the threshold
judgment of the three features. They achieve an
85.2% detection rate which proves that this method
is easy and accurate for the estimation of the
population density of RPH in palay areas. In [5], it
designs an automatic measurement system to also
detect and count the population of the brown
planthopper. They use Image Processing Technique
such as image processing, binarization and edge
detection. The system detects the adult BPH at the
accuracy rate of 69.76%. The study of [7] uses
various techniques to count the number of pests.
These techniques involve motion estimation and
multiple frame verification. Cameras were used [12]
to capture still images of mono crops infested with
coffee berry or aphids. Various algorithms were used
in the study such as k-means, fuzzy c-means and
Expectation-Maximization which easily identified
the pest infested areas of the crop. In [13], it uses
camera to capture the images of the infested leaf and
the bio-aggressors were automatically detected using
computer vision techniques. The processing of image
is done through various processes such as filtering,
segmentation and object extraction. However, object
detection through the use of Haar feature-based
cascade classifiers can easily detect pests. The study
conducted [14] showed a simple yet efficient way to
detect and extract pests in the palay practice farm in
Pampanga. The researchers used background
modelling that determined the presence of pests and
median filter to remove noise that was produced by
different lightning conditions. This paper is similar
to the research of [1] but the researchers used
microscopic camera, microcontroller and develop
Haar feature-based cascade classifier [15].
In recent years, the BPH has caused extensive
damage to rice crop in Asia namely Brunei,
Bangladesh, China, Malaysia, Sri Lanka, Thailand
and Vietnam [16]. But the insect’s large scale
damage has been reported in India, Indonesia, Sri
Lanka and the Philippines. The BPH is regarded by
many as the number-one insect pest of rice in Asia
today. Thus, if not detected early, its unpredictable
infestation could result to severe damage in palay and
loss of production for farmers. Also, BPH’s sudden
occurrence may force the farmers to use unnecessary
insecticides which could lead to its outbreak because
of the death of natural enemies [17].
The general objective of the study is to develop a
device that can detect BPH in palay. Specifically, this

7. used a microscopic camera to capture video feeds of
the BPH on palay plant and to develop OpenCV-
Python code using Haar feature-based cascade
classifier algorithm.
The system was beneficial to the farmer because
it provided a rapid system for detection of BPH
through image processing. It also gave the farmer
sufficient time to plan and employ appropriate pest
control methods such as natural chemical and
biological control, integrated pest management and
other methods which can be very beneficial to the
farmer and most importantly to the environment.
The study was designed to detect BPH pest of
palay crop at reproductive stage under controlled
environment. This followed the recommended
fertilizer and temperature requirements and the usual
farm practice and care for this crop. The system
captured the video images of the BPH pests using
microscopic camera and not the image of the damage
it caused to the plant. The obtained images were then
programmed into the system through Haar feature-
based cascade classifiers. Necessary interventions
were done by the farmer and not by the system.
II. METHODOLOGY
A. Evaluation Phase
In the evaluation phase of the research, the
researcher gathers the necessary equipment to be
used in obtaining the images of the BPH. The
International Rice Research Institute (IRRI) in Los
Baños, Laguna provided the needed pests, palay
plant and its container. The insects and palay plant
are kept in a cage made of Mylar plastic film that
encloses the cage and organza cloth that serves as the
door (front) and exhaust (back) of the cage. The
experiment is done using microscopic camera to
capture video and still images of the BPH and is
conducted at the insectary of IRRI. Pot culture rice is
used with seeds of Taichung Native 1 (TN1) variety.
The age of the palay plant is 50-60 days after
transplanting. The BPH is 2-3 day old adults and
reared in covered cages under natural temperature.
All experiments are conducted under the same
natural temperature.
Per container, approximately 200 adult BPH is
put into the palay plant that is on its reproductive
stage. Reproductive stage refers to the stage from
booting to blooming, flowering, heading, tasseling
and milking stage (46 to 75 days after crop
establishment).
Object detection using Haar feature-based
cascade classifier is used in the study. This is an
effective object detection method that was proposed
by Paul Viola and Michael Jones in their paper
entitled “Rapid Object Detection using a Boosted
Cascade of Simple Features” [19]. A cascade
function is trained from a number of positive and
negative images. To create cascade classifiers based
on Haar-like features, 200 positive and 200 negative
sample images are trained which were captured using
the microscopic camera. The positive images are
images that contain the BPH and the negative images
are those that do not contain the object. Then the
positive images are marked. These are the rear part
(butt) of the BPH. A vector data file was created
based on the positive marked images which contain
the names of positive images and the location of the
objects in each image. The classifier is then trained
and ran using an OpenCV file. Haar-like features
shown in Fig. 1 are used. Through subtracting the
sum of pixels that are under the white rectangle from
sum of pixels under the black rectangle, a single
value is obtained from each feature.
Fig. 1. Examples of Haar-liked Features
Fig. 2. (a) BPH as the Positive images and (b) Green Leaf
Hopper and Black Bugs as the Negative Images. There were 200
positive and 200 negative sample images that were trained and
created a cascade classifiers based on Haar-liked features.
Shown in Fig. 2 are some of the positive images
and negative images used in the system.
Detecting the BPH is the sensor node. It is
composed of microscopic camera and Raspberry Pi
3 Model B. The microscopic camera is placed 5
inches away from the palay plant.
B. Development Phase
In the development phase, the researchers
gathered all the necessary materials/equipment used
in the study and setup the needed hardware. The
sensor nodes are set-up according to the required
operation and structure. The system programs are
developed in this phase and tested if it runs properly.
For Raspberry Pi, the latest version of Raspbian
Operating System (OS) which is Raspbian Stretch is
installed. The application and system files are also
updated. After which, the Python-OpenCV package
is installed.
The Raspberry Pi as sensor node is developed
with test code using Python OpenCV library with the
(a)
(b)

8. generated cascade classifier to detect the BPH from
video feed.
C. Implementation Phase
In this phase, all the necessary materials and
equipment are installed. The researchers also set-up
the farm prototype with the BPH pests and integrates
it with the program. Test run is done if the system is
correctly running according to the desired output.
D. Testing Phase
In the testing phase, the system is tested if
properly detects BPH pest. Initially, the microscopic
camera is set by calibrating its focus to the sharpest
detail possible. This will give a clear and define
image of the pest. The camera is set 5-inch away
from the plant. Once set, the sensor node is initialized
to process the captured data.
III. RESULTS AND DISCUSSION
The researcher performed detection of the BPH
through getting a random sample of 50 BPH and
caged it individually as shown in Fig. 3. Each cage
contained one (1) BPH pest. The system was set-up
and the camera was focused to the sharpest detail
possible. The researcher also tested the validity of the
system by testing other pests such as 10 Green Leaf
Hoppers (GLH) and 10 black bugs.
Fig. 3. Individual cages of the insect that were used during
actual testing. Each cage contained one (1) insect and labeled
accordingly.
The following tables were used to show that the
proposed system can detect captured images of the
BPH through Haar feature-based cascade classifier.
TABLE I. CONFUSION MATRIX FOR TESTING OF BPH
AND NON-BPH
In the confusion matrix, of the 50 actual BPHs,
the system predicted that two were not BPH, and of
the 10 Green Leaf Hoppers and 10 Black Bugs, it
predicted that all were not BPH. Fig 7 shows the
images that have been tested by the system to get the
percentage accuracy of classifying the BPH correctly
as shown in figure 6.
Fig. 4. Example of Test Captured Images for (a) BPH, (b) GLH
and (c) Black Bug. This shows that the BPH were correctly
boxed and detected. The GLH and Black Bugs were also
predicted as non-BPH
The accuracy from the confusion matrix table can
be computed using the formula,
Accuracy (ACC) = Σ True positive + Σ True
negative / Σ Total population (1)
Thus, the accuracy of the detection of BPH is,
Accuracy = ((48+20)/70) x 100 = 97.14%
This means that the early warning device is
capable of detecting the BPH for about 97 percent.
IV. CONCLUSION
From this paper, an accurate detection of BPH is
presented, tested and implemented. The prototype
was designed using microscopic camera to
efficiently capture the images of the BPH and
installed in strategic locations within the palay area
under study. This is connected to the Raspberry Pi
which is the main controller of the node. It is coded
in Python with OpenCV library that is used for
computer vision. The researchers used the image
processing technique such as Haar feature-based
cascade classifier for pre-processing of the images.
This process gave percentage accuracy of 97.14%
n=70 BPH Non-BPH
BPH TP=48 FP=2 50
Non-BPH FN=0 TN=20 20
48 23 70
Actual
Predicted
(a)
(b) (c)

9. were the differences vary from the manner the image
is captured.
Based on the findings and results of the trainings
and testing that have been done, the researchers
recommend the following for the improvement of the
system: for better image quality, it is better to use
camera with higher specification especially on
magnification capability and to include in the system
the ability to determine the number of BPH present
in the area for appropriate intervention to be done by
the farmer.
ACKNOWLEDGMENT
The authors are thankful to the entomologists
under Crop and Environmental Sciences Division in
IRRI, Los Baños, Laguna for providing necessary
facility and materials to undertake this research.
Also, this research has been funded by the
Engineering Research and Development for
Technology under the Department of Science and
Technology (DOST).
REFERENCES
[1] V. A. Dyck, and B. Thomas, “The brown planthopper
problem. In: Brown planthopper: threat to rice production
in Asia,” International Rice Research Institute, Los Baños,
Laguna, Philippines. Pp. 3-17: 1979.
[2] G.R. Wu, X.P. Yu, L.Y. Tao, “Long-term forecast on the
outbreak of brown planthopper (Nilaparvata lugens Stal)
and white-backed planthopper (Sogatel la f urcif era
Horvath). Sci. Agric. Sinica Pp. 30, 26–30: 1997
[3] O. Mochida, and T. Okada, “Taxonomy and biology of
Nilaparvata lugens (Hom: Delphacidae). In: Brown
planthopper: threat to rice production in Asia,” Los Baños
(Philippines): International Rice Research Institute. P 21-
43.
[4] Y. Qing, X. Ding-xiang, L. Qing-jie, Y. Bao-jun, D. Guang-
qiang, and T. Jian, “Automated Counting of Rice
Planthoppers in Paddy Fields Based on Image Processing,”
Journal of Integrative Agriculture, August 2014, 13(8):
1736-1745.
[5] N. Mongkolchart, and M. Ketcham, “The Measurement of
Brown Planthopper by Image Processing,” International
Conference on Advanced Computational Technologies and
Creative Media (ICACTCM’2014).
[6] S. Xu, Z. Zhou, H. Lu, X. Luo, Y. Lan, Y. Zhang, and Y.
Li, “Estimation of the Age and Amount of Brown Rice Plant
Hoppers Based on Bionic Electronic Nose Use,” Sensors,
14, 18114-18130; doi:10.3390/s141018114: 2014
[7] Y. Yang, B. Peng and J. Wang, “A System for Detection
and Recognition of Pests in Stored-Grain Based on Video
Analysis,” vol. 344, D. Li, Y. Liu and Y. Chen, Eds. Berlin,
Heidelberg: 2011.
[8] C. Priya, K. Praveen, and A. Srividya, “Monitoring of Pest
Insect Traps Using Image Sensors & Dspic,” International
Journal of Engineering Trends And Technology, vol 4 Issue
9.
[9] O. Lopez, M. Rach, H. Migallon, M.P. Malaumbres, A.
Bonastre, J.J. Serrano, “Monitoring Pest Insect Traps by
Means of Low-Power Image Sensor Technologies,”
Sensors ISSN, 1424-8220: 2012.
[10] N. Gopal, “Micro-Controller Based Autoirrigation and Pest
Detection Using Image Processing International Journal of
Advances in Agricultural Science and Technology, Vol.3
Issue.1, Pp. 37-42: March- 2016.
[11] F. Ghobadifar, A. Wayayok, M. Shattri, and H. Shafri,
“Using SPOT-5 images in rice farming for detecting BPH
(Brown Plant Hopper),” IOP Conf. Series: Earth and
Environmental Science 20, 012015: 2014.
[12] M. Krishnan, G. Jabert, “Pest Control in Agricultural
Plantations Using Image Processing,” IOSR Journal of
Electronics and Communication Engineering (IOSR-
JECE), vol. 6, issue 4, pp. 68-74, 2013
[13] G. Bhadane, S. Sharma, and V. Nerkar, “Early Pest
Identification in Agricultural Crops using Image Processing
Techniques,” International Journal of Electrical, Electronics
and Computer Engineering, 2(2): 7 -82(2013), ISN No.
(Online): 27-2626.
[14] J. Miranda, B. Gerardo, and B. Tanguilig III, “Pest
Detection and Extraction Using Image Processing
Technique,” International Journal of Computer and
Communication Engineering, vol. 3, no. 3.
[15] F. Jalled, and I. Voronkov, “Object Detection Using Image
Processing,” Department of Radio Engineering &
Cybernetics, Moscow:2016
[16] J.L.A. Catindig, G.S. Arida, S.E. Baehaki, J.S. Bentur, L.Q.
Cuong, M. Norowi, W. Rattanakarn, W. Sriratanasak, J.
Xia, and Z. Lu, “Situation of planthoppers in Asia,
“International Rice Research Institute, Los Baños, Laguna,
Philippines. Pp. 191-220: 2009.
[17] K.L. Heong, K.H. Tan, C.P.F. Garcia, Z. Liu, and Z. Lu,
“Research Methods in Toxicology and Insecticide
Resistance Monitoring of Rice Planthoppers”, International
Rice Research Institute, Los Baños, Laguna, Philippines.
Pp. 3-17: 2013.
[18] O. Mochida, and T. Okada, “Taxonomy and biology of
Nilaparvata lugens (Hom: Delphacidae). In: Brown
planthopper: threat to rice production in Asia,” Los Baños
(Philippines): International Rice Research Institute. P 21-
43.
[19] P. Viola, M.J. Jones, “Rapid object detection using a
boosted cascade of simple features,” CVPR ’01:
Proceedings of the Conference on Computer Vision and
Pattern Recognition, Los Alamitos, California, USA. Pp
511-518: 2001.