THERMAL IMAGING FOR PESTS DETECTING

1
THERMAL IMAGING FOR PESTS DETECTING
By Jwan M Aldoski Prof . Dr. Shattri B
Mansor Dr.Helmi Zulhaidi Bin Mohd Shafri
Department of Civil Engineering , Faculty of
Engineering, Universiti Putra Malaysia, 43400
UPM Serdang, Selangor Darul Ehsan. Malaysia.
2
Content

• Introduction
• Remote Sensing
• Thermal Imaging System (Thermography)
• Components of Thermography
• Concepts and Issue of Thermography
• Thermography Applications
• Thermography Application for Pest Detection

3
Introduction
The decrease in agricultural productivity can be
attributed to a variety of reasons .
4
Introduction
(Barber and Black. 2007)
5
Remote Sensing
It is the science of acquiring information about
the earths surface without physically touching
it .
(Campbell. 2002 Lillesand and Chipman. 2014)
6
Remote Sensing Platforms
7
Remote Sensing Sensors
8
Remote Sensing Systems
(Lillesand and J Chipman. 2014)
9
Thermography ( Thermal Remote Sensing)

• Thermography is a non-destructive technique used
  to determine thermal properties of any objects of
  interest.

10
Thermal system differs from other remote sensing
systems

• Measuring emitted radiations of the target.
• It does not require an illumination (Light)
  source.
• It is possible to obtain temperature of any
  particular region of interest .
• The repeat- ability of temperature measurements
  is high.
• Thermal cameras are easy to handle and highly
  accurate temperature measurements.

(Roselyne and Ahmed. 2014)
11
Components of Thermal Imaging Systems
12
Thermal Sensors
Handheld
Airplane or Satellite
13
Camera Model Manufacturer Camera Model Manufacturer Spectral Range, mm Temperature Range ThermalSensitivity ImageSize FrameRate
AGEMA 570 LW FLIR Systems,Oregon, USA 812 -20C to 500C-20C to 1,500C (with filter) 0.1C at 30C 320240 60 Hz
AGEMA 880 LW FLIR Systems,Oregon, USA 8-12 20C to 1,500C - 0.7 K at 30C NA 25 Hz
Infra-Eye 102A Fujitsu, Tokyo, Japan 8-14 NA NA NA NA
InframetricsModel 760 Inframetrics,Massachusetts, USA 812 -20C to 400C20C to 1,500C (with filter) 0.1C at 30C NA 30 Hz
IR Snapshot 525 Alpine Components,East Sussex, UK 812 0C to 350C -50C to 650C 0.1C at 30C 120120 NA
Model D500 Raytheon Inc.,Waltham, MA 714 NA NA 320240 NA
14
Concepts of Thermography
(Prakash. 2000)
15
Electromagnetic (EM) Spectrum
Visible
Infrared
(Frank and Samson. 2015)
16
Electromagnetic (EM) Spectrum regions
Regions Regions Regions Wavelength Frequency
Gamma Rays Gamma Rays Gamma Rays lt1 10-11 nm gt3 1019
X- Rays X- Rays X- Rays 1 10-11 -1 10-8 3 1016 -3 1019
Ultraviolet Ultraviolet Ultraviolet 0.0010.4 um 7503000 THz
Visible Visible Visible 0.40.7 um 430750 THz
  Infrared Reflected IR Near Infrared Shortwave Infrared 0.71.3 um 1.33.0 um 230430 THz 100230 THz
  Infrared Thermal IR Intermediate Infrared 38 um 38100 THz
  Infrared Thermal IR Thermal Infrared Far Infrared 814 um 14 um 1 mm 2238 THz 0.3--22 THz
  Radio wave Microwave Millimeter 110mm 30--300 GHz
  Radio wave Microwave Centimeter 110 cm 330 GHz
  Radio wave Microwave Decimeter 0.11 m 0.33 MHz
  Radio wave Very Shot Wave Very Shot Wave 1-10 m 30300MHz
  Radio wave Short Wave Short Wave 10100m 330MHz
  Radio wave Medium Wave Medium Wave 0.11 km 0.3-3MHZ
  Radio wave Long Wave Long Wave 110 km 30-300KHz
  Radio wave Very Long Wave Very Long Wave 10100km 330KHz
(Frank and Samson. 2015)
17
Atmospheric Windows
The earths atmosphere is not completely
transparent to electromagnetic radiation, because
of the barriers, particles and gases.
(Lillesand and Chipman. 2014)
18
Issues of Thermograph
(Prakash. 2000)
19
(No Transcript)
20
(No Transcript)
21
Stored Grain Insects

1.  Rusty Grain Beetle

  The rusty grain beetle is the most serious and
common pest of stored grain. Manickavasagan et
al., 2008 used an infrared thermal imaging
system to detect insect in wheat .
22

• Temperature distribution was highly correlated
  with the respiration rate (r0.830.91).
• The classification and linear function used as
  image processing. The overall classification
  accuracy was 77.7 to 83 .
• They concluded that thermal imaging has the
  potential to identify whether the grain is
  infested or not, but is less effective in
  identifying which developmental stage is present.

1. Control panel 2. Microwave applicator 3.
Conveyor 4. Thermal camera (camera ThermaCAM
SC500) 5. Data acquisition system
23
b) Cowpea Seed Beetle
Cowpea seed beetle is a common pest of stored
legumes . Chelladurai et al., (2012) applied an
infrared thermal camera to acquire thermal images
of un-infested and infested beans with
different stages of insect with completely
infested beans.
24

• The classification models (linear and quadratic
  discriminant (LDA and QDA) classification models)
  were used as image processing techniques from
  thermal images.
• The results have proven
• Thermal imaging has a potential to detect the
  Cowpea seed beetle .
• Classification accuracies of the LDA models were
  low than the QDA classifiers . However , the QDA
  classification model correctly identified more
  than 80 beans infested with initial stages of
  insect.

Thermal imaging system 1. Plate heater, 2.
Thermal camera, 3. Close-up lens, 4. Thermocol
box with ice 5. PID controller, 6. Data
acquisition system
25
Young Trees Insect Detection
a) White Pine Cone Beetle
 
The white pine cone beetle is found throughout
the range of eastern white pine tree , from
eastern Canada. Detection of beetle attacks in
white pine seed orchards have been addressed
using thermal remote sensing by Stephen Takács et
al., in 2008.
26

• In conclusion
• 1. There is a difference in temperature between
  pine cones and the needles about 15 C warmer
  than the surrounding needles.
• 2. The pine cones effected by beetles can be
  detect

27
b) Australian fire-beetle
The Australian fire-beetle bears its name
because it is distributed after forest
fires. Schmitz, (2015) applied IR FlexCam T IR
camera (Goratec Inc.) to obtain thermal imagery
which used to describe the behavior of fire
beetle on a freshly burnt area after a fire and
to detect the coexist region of beetle.
28

• The result, indicate
• The thoracic surface temperature 46C of a sun
  basking beetle, while it was 36C of the beetle
  resting on the border between the sunny and
  shaded region of a burnt stem.
• 2. Detection of beetle area after the fire also
  been studied by Schmitz, (2015) for detecting the
  coexist area of beetles in burnt area.

29
c) Red Palm Weevil (RPW)
The weevil is the worlds worst pest of palm trees
Thermal system applied by Golomb (2015) in the
field to detect RPW larvae
Thermal system applied by Soroker, 2013 in green
house to detect RPW larvae
30

• Soroker (2013) results showed that in some
  infested palm trees the RPW larvae caused water
  stress, which was reflected by both higher canopy
  temperature compared with healthy trees.
• Golomb (2015) results partially showed that the
  RPW creates water stress and affects canopy
  temperature. Successfully detected the infected
  trees, which was similar to Soroker (2013)
  results.

31
Conclusion
32

• Thank You