INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES
Volume 7, No 3, 2017
© Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0
Research article ISSN 0976 – 4380
Submitted on November 2016 published on February 2017 321
Unmanned Aerial Vehicle (UAV) in Agriculture with Crop CAM
Finite Element Analysis (FEA) Study Balaji Sethuramasamyraja and Bregan Gray
Department of Industrial Technology, California State University, Fresno
ABSTRACT
The CropCAM is an agricultural and industrial technology wing aircraft which takes images
of fields, crops and other parts of an agricultural operation. The winged aircraft is
programmed to fly through while taking photos and land at the end of the flight
autonomously. The images provided high-resolution based digital images from the GPS for
precision agriculture. It has the option to arrange images in any position to show where they
were taken to create a single image of the area. Primary software that was used combines
pictures from the digital camera into a set of reference pictures geocentrically. Each time the
camera and a GPS lock, the autopilot started recording into a data logger and collected data
whether it's throughout the flight or through the pictures taken. By Geo-registration process,
ground control points were determined by two methods – first was the ground control points
which was best for turnaround time and to correlate different images of any area for
agriculture. Next were the ground control points that was used for finding locations recorded
in the GPS from the image (Connor, D. Loomis, 2011). With these images, it was useful for
agricultural purposes to accurately and approximately decide whether to spray water at a
particular spot or not, have a permanent record of crop damage data throughout anywhere,
and assist in crop locations which are great way points. This was useful for industrial
technology because the images were compared to satellite imagery. The typical spatial
resolution was better than some of the satellite systems. The camera was adjusted in the lab to
make sure that the entire area is acquired.
Key words: Precision agriculture, Ground Control, Spatial resolution, CropCAM, Stress.
1. Introduction
The CropCAM is an agricultural and industrial technology wing aircraft that takes images of
fields, crops and other parts of an agricultural operation. The winged aircraft was
programmed to fly while taking photos and land at the end of the flight autonomously. The
images provided high-resolution digital images from the GPS for precision agriculture
(Connor, D. Loomis et al, 2011). You also have the option to use the software that is included
which allows you to arrange images in any position to show where they were taken to create
a single image of the area. UAV ortho-mosaics are becoming an important tool for early site-
specific weed management (ESSWM), as the discrimination of small plants (crop and weeds)
at early growth stages is subject to serious limitations using other types of remote platforms
with coarse spatial resolutions, such as satellite or conventional aerial platforms (Candan,
2014). Unmanned aerial vehicle (UAV) also called as unmanned aerial system (UAS) is an
unpiloted aircraft. Unmanned aerial vehicles were controlled and made to fly based on the
complex dynamic automation systems or pre-programmed flight plans. Nowadays, UAVs are
currently used in many military roles that includes attack and reconnaissance (Zainuddin, K,
2015).
Unmanned Aerial Vehicle (UAV) in Agriculture with Crop CAM Finite Element Analysis (FEA) Study
Balaji Sethuramasamyraja and Bregan Gray
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 322
The primary software that was used combines pictures from digital camera into a set of
reference pictures geocentrically. Every time this camera and a GPS lock, the autopilot
started recording into a data logger and collected data whether it's throughout the flight or
through the pictures taken. In the Geo-registration process, we determined and used ground
control points by at least two methods. The first was ground control point which was the best
for turnaround time and to correlate different images of any area for agriculture. Also the
ground control points were used to find locations recorded in the GPS from the image
(Christiansen, J. 2009). The next method was the marking method for agriculture use and it
was environmental friendly. This reduced the time required to retrieve the ground control
points. The benefited the GPS aerial images being used for crop analysis. Throughout the
growing season, the camel for vine based digital images was on demand. The typical spatial
resolution was better than some of the satellite systems. The camera was adjusted in the lab to
make sure that the entire area is acquired (Hassan, F. M, 2010).
2. Materials and Methods
Before working with the CropCAM, few basics had to be learnt that includes detailed
understanding of specifications, parts, the required equipment, learning the tools, becoming
familiar with all the equipment, parts, tools, and figuring out the problems that are likely to
occur. Next was the safety procedures, guidelines to operate the CropCAM, and site
requirements for the imagery. Followed by assembling cam, getting familiar with the
different tools on the plane court and being familiar with the tools on the camera. Next step in
the project process was to prepare for the flight. It was made sure that everything is up-to-
date on every part of the camera before take-off. During flight, camera was checked and then
after the flight the winged craft was taken, copy of images were taken outside, transferred the
dialogue and data logged in to the image software, collected data and solved the problems
(Gray, C., and Larson, E.,2008). The materials and methods includes:
1. Esri ArcGIS 2. CropCAM 2012 3. MapInfo 4. ErMapper
5. Manifold 6. Ozi Explorer
7. Cropcam UAV 8. GPS
9. Fixed Wing UAV 10. Flight Simulator
11. Solidworks 12. AMPIPS
ArcGIS was used for working with maps and geographic information. For this application,
we compiled geographic data and analyzed field information and managed this information in
a database (Law, M. and Collins, A., 2013). For manifold application, the map info was used
to understand the output data and trends that we had throughout the images. It was also used
to create maps. Our fixed wing UAV and CropCAM UAV was used for test runs. During the
aerodynamic data extraction process, it was found that aerodynamic pressure cannot be
directly applied to the vehicle structure in ANSYS software, as different meshing schemes
were used and location of nodes were not the same in CFD/ FE analysis. A methodology was
therefore devised upon data fitting technique using the Artificial Neural Networks (Mazhar,
F., and Khan, A.M., 2010). The finite element analysis of the impeller was carried out by
using ANSYS Workbench. The UAV impeller was the key component of aircraft engine.
Because of its special performance, it was under the action of centrifugal force, gas pressure
and thermal stress. Flight simulator was used for a test run to see if the aircraft could be
controlled manually if needed. For the software, the UAV was connected to the program and
Unmanned Aerial Vehicle (UAV) in Agriculture with Crop CAM Finite Element Analysis (FEA) Study
Balaji Sethuramasamyraja and Bregan Gray
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 323
picked out a few vantage points for the aircraft to take the digital photos. This determined
more data. Solid works and AutoCAD was used to draw the winged aircraft and use the skills
to put dimensions of every part used. Solid Works was used for finite element analysis to
determine the maximum stress and strain for each part of the UAV (Hassan, F. M, 2011).
Specifications of CropCAM: Table 1: Specifications of CropCAM
Figure 1: CropCAM
3. Results and Discussion
Length 4 ft. Batteries 2100 mah 3 cell
LiPo
Wing
Span
8 ft. Altitude 400 min- 2000
max
Weight 6 lbs. Flight
Duration
55 minutes
Engine Brushless
Electric
Von Mises Stress
Stress =
Force(N)/Area(m^2)
Stress Formula,
First and Third Principle
Stress,
Von Mesis Stress,
Displacement
(S),
Where, u = initial velocity, v = final velocity, a =
acceleration and t = time
Unmanned Aerial Vehicle (UAV) in Agriculture with Crop CAM Finite Element Analysis (FEA) Study
Balaji Sethuramasamyraja and Bregan Gray
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 324
3.1 CropCAM Drone Analysis
The goals were to develop a feature to fly autonomously in enclosed or large spaces
and to support communication of UAV and computer software.
Communication Settings:
Step 1: Set COMM Part:
• The Crop Cam uses the Top Model CZ Electra airframe
• Lightweight Fuselage, Cabin and wingtips made of colored fiberglass.
• Polyetherethekerketone (PEEK)- balsa finished with wings, Aileron, Elevator, Rudder
Step 2: Set Measurements:
• Measure Cropcam after assembling
Step 1: Set COMM Port Step 2: Set Measurements
Step 3: Design all parts on AutoCAD and solid works.
Step 4: Add three views to all parts on AutoCAD.
Step 3: Select aircraft Step 4: Lock GPS
Step 5: Apply material to all parts in solid works/Autodesk inventor
Step 6 -8: Mate, lock, and Export assembly drawing from solid works to Autodesk inventor
Unmanned Aerial Vehicle (UAV) in Agriculture with Crop CAM Finite Element Analysis (FEA) Study
Balaji Sethuramasamyraja and Bregan Gray
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 325
Step 5: Verify Autopilot Step 6: Confirm AGL Board
Step 7: Configure Servo Step 8: Test Servo Zero on All Parts
Step 9: Apply Constraints to all parts
Step 10 – 12: Add load, contacts, pressure to simulated part on order desk mentor.
Step 9: RC Test Step 10: Altitude Settings
Step 11: Configure Throttle Step 12: Set Default speed
Unmanned Aerial Vehicle (UAV) in Agriculture with Crop CAM Finite Element Analysis (FEA) Study
Balaji Sethuramasamyraja and Bregan Gray
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 326
Step 13: Configure Battery
Figure 2: Communication Settings (Step 1 to Step 13)
Physical: Table 2
Mass 6.9905 lb. mass
Area 2763.97 in^2
Volume 619.171 in^3
Center of Gravity
x=0.186206 in
y=6.42272 in
z=-4.282 in
Finite element (FE)-based topology optimization of mechanically stressed structures was
realized as an effective technique to generate optimal design concepts. Optimization
algorithm optimizes the user-defined objectives (minimize the compliance, strain energy etc.)
under specified design constraints (volume fraction, maximum stress etc.). Material
redistribution (removing of finite elements) in design domain (3D FE model) was
accomplished by determining the optimal load paths (Saleem, W et al, 2014).
Remote sensing uses a crop-CAM by using spectral reflectance and digital images can be
nondestructive, rapid, cost-effective and reproducible technique to determine damages by
bugs (Mirik, M., et al. 2006). Using Hyper-spatial resolution, natural color digital aerial
photography was acquired from a low-altitude UAS as input images (Zhang, S., et al. 2016).
With the current Differential Global Positioning System, satellite and airplane images hardly
meet the geo-referencing requirement. This was met by geo-referenced terrestrial targets
(Candon, G.D., et al., 2011).
Finite Element Analysis Boundary Conditions and Settings
Table 3: Mesh settings
Avg. Element Size (fraction of model diameter) 0.1
Min. Element Size (fraction of avg. size) 0.2
Grading Factor 1.5
Max. Turn Angle 60 degrees
Create Curved Mesh Elements No
Use part based measure for Assembly mesh Yes
Unmanned Aerial Vehicle (UAV) in Agriculture with Crop CAM Finite Element Analysis (FEA) Study
Balaji Sethuramasamyraja and Bregan Gray
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 327
Table 4: Material(s)
Material Polyetheretherketone (PEEK)
General
Mass Density 0.047 lb.
mass/in^3
Yield Strength 0 psi
Ultimate Tensile Strength 13778.6 psi
Stress
Young's Modulus 565.65 ksi
Poisson's Ratio 0.4
Shear Modulus 202.017 ksi
Part Name(s) Rudder, Aileron, Elevator, Throttle, Left Flap, Right
Flap, Right Aileron
Material Polystyrene/Polyetheretherketone (PEEK)
General
Mass Density 0.04 lb. mass/in^3
Yield Strength 6294.64 psi
Ultimate Tensile Strength 6497.69 psi
Stress
Young's Modulus 464.12 ksi
Poisson's Ratio 0.353
Shear Modulus 171.51 ksi
3.2 Operating conditions
Figure 3: Body Loading and Face Selection
Table 7: Force and Bearing Load
Load
Type
Bearing Load 1, 2 and
3
lb. force
Force 1 and
2
lb. force
Gravity
in/s^2
Body
Loads
in/s^2
Magnitude 0.1 0.1 0.1 20.0 10.0 386.2 6.0
Unmanned Aerial Vehicle (UAV) in Agriculture with Crop CAM Finite Element Analysis (FEA) Study
Balaji Sethuramasamyraja and Bregan Gray
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 328
Vector X 0.0 0.0 0.0 20.0 0.0 8.8 0.25
Vector Y -0.1 -0.1 -0.13 0.0 -10.0 -385.9 -5.9
Vector Z -0.0 0.0 0.0 0.0 0.0 -10.3 0.0
Selected Face(s)
Figure 4: Force and Bearing Load
Table 8: Pressure- 1 and Fixed Constraint
Load Type Magnitude
Pressure 40.000 psi
Constrain Type Fixed Constraint
Constraint type Frictionless Constraint
Selected Face(s)
Figure 5: Pressure - 1 and Fixed Constraint
For first and second principal stress, the maximum allowed for failure was at 4.801 KSI
For 3rd principle Stress, the max shear tension was at .075 KSI in the maximum minimum
Unmanned Aerial Vehicle (UAV) in Agriculture with Crop CAM Finite Element Analysis (FEA) Study
Balaji Sethuramasamyraja and Bregan Gray
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 329
sheer tension was at -9.466 KSI. For Displacement, the max was at 1.194 in or 0.0303276 m
and the min was at 0.
Table 9: Reaction Force and Moment on Constraints
Constraint
Name
Reaction Force Reaction Moment
Magnitude
lb. force
Component
(X, Y, Z) lb.
force ft.
Magnitude
lb. force
Component
(X, Y, Z) lb.
force ft.
Fixed
Constraint:1 20.5
-20.38
8.4
0.39
8.76 7.59
0.43 3.49
Frictionless
Constraint:1 6113.9
0
1638.2
198.82.
61.9 0
0 -1626.09
1st Principal Strain 2nd Principal Strain 3rd Principal Strain
Figure 6: 1st, 2nd and 3rd Principle Strain
Contact Pressure Contact Pressure X Contact Pressure Y Contact Pressure Z
Figure 7: Contact Pressures of X, Y and Z
Unmanned Aerial Vehicle (UAV) in Agriculture with Crop CAM Finite Element Analysis (FEA) Study
Balaji Sethuramasamyraja and Bregan Gray
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 330
1st Principal Stress 2nd Principal Stress 3rd Principal Stress Displacement
Figure 8: 1st, 2nd and 3rd Principle Stress along with Displacement
The maximum allowed Von Mises stress for failure was at a threshold of 8.5 KSI. Safe
Factor and Pressure were calculated along with obtaining results for stress on Crop Cam and
more data was collected using by flying the Crop Cam on Micro Pilot Horizon. A final report
for all findings was created.
Table 10: Result Summary
Name Minimum Maximum
Volume 409.8 in^3
Mass 6.24 lb. mass
Von Mises Stress 0.0 ksi 8.58 ksi
1st Principal Stress -0.53 ksi 4.8 ksi
3rd Principal Stress -9.46 ksi 0.07 ksi
Displacement 0 in 1.19 in
Safety Factor 1.42 ul 15 ul
X Displacement -0.05 in 1.16 in
Y Displacement -0.8 in 0.04 in
Z Displacement -0.89 in 0.00 in
Equivalent Strain 0.00 ul 0.014 ul
1st Principal Strain 0.00 ul 0.0089 ul
3rd Principal Strain -0.01 ul -0.00 ul
Contact Pressure 0 ksi 13.78 ksi
Contact Pressure X -0.92 ksi 0.68 ksi
Contact Pressure Y -13.00 ksi 12.16 ksi
Contact Pressure Z -4.48 ksi 6.37 ksi
4. Conclusion
These images help agriculturist to accurately and approximately decide whether to spray
water at a particular spot or not, to have a permanent record of crop damage data throughout
anywhere, and assist in crop locations which were great way points. This was found to be
useful for industrial technology because these images were compared to satellite imagery.
The typical spatial resolution was better than some of the satellite systems. The camera was
Unmanned Aerial Vehicle (UAV) in Agriculture with Crop CAM Finite Element Analysis (FEA) Study
Balaji Sethuramasamyraja and Bregan Gray
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 331
adjusted in the lab to make sure that the entire area is acquired. The Crop Cam was used on
agricultural fields to obtain data and it will be used for agricultural purposes.
5. References
1. Connor, D. Loomis, R., Cassman, K., and Loomis, R. (2011). Crop Ecology:
Productivity and Management in Agricultural Systems. Cambridge; New York:
Cambridge University Press.
2. Precision Agriculture. (n.d.). doi:10.1007/11119.1573-1618
3. Candan, G.D. (2014). Assessing the accuracy of mosaics from unmanned aerial
vehicle (UAV) imagery for precision agriculture purposes in wheat (5th ed, pp. 44-56).
Precision Agriculture
4. Zainuddin, K., Ghazali, N., and Arof, Z. M. (2015). The feasibility of using low-cost
commercial unmanned aerial vehicle for small area topographic mapping. 2015 IEEE
International Conference on Aerospace Electronics and Remote Sensing Technology
(ICARES). doi:10.1109/icares.2015.7429825
5. Christiansen, J. (2009). Get to know Inclined Planes. New York, NY: Crabtree Pub.
Company
6. Hassan, F. M., Lim, H. S., and Jafri, M. Z. (2010). Cropcam UAV images for land
use/land cover over Penang Island, Malaysia using neural network approach. Earth
Observing Missions and Sensors: Development, Implementation, and
Characterization. doi:10.1117/12.869454
7. Crop Physiology: Applications for Genetic Improvement and Agronomy. (2009).
Amsterdam: Boston: Elsevier / Academic press.
8. Gray, C., and Larson, E. (2008). Project management: The managerial process (4th
ed., pp. 422-424). Boston: McGraw- Hill/Irwin
9. Law, M., and Collins, A. (2013). Getting to know ArcGIS for desktop (3rd ed., pp.
529-674). Redlands, Calif.: ESRI Press
10. Mazhar, F., and Khan, A.M. (2010). Structural Design of a UAV wing using Finite
Element method. Retrieved from Structural Dynamics and Materials Conference.
11. Autodesk Inventor., (2016). UAV stress analysis (pp. 1-75). Fresno, California:
Bregan Gray.
12. Hassan, F. M., Matjafri, M. Z., Lim, H. S., and Mustapha, M. R. (2011).
Performances of frequency-based contextual classifier for land use/land cover using
Cropcam UAV data. Proceeding of the 2011 IEEE International Conference on Space
Science and Communication (IconSpace). doi:10.1109/iconspace.2011.6015843
13. Zhang, C., and Kan, C. (2014). The reverse reconstruction and Finite Element
Analysis of the UAV Impeller. International Journal of Science and Research.
Unmanned Aerial Vehicle (UAV) in Agriculture with Crop CAM Finite Element Analysis (FEA) Study
Balaji Sethuramasamyraja and Bregan Gray
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 332
14. Saleem, W., Ejaz, H., Khan, M., and Asad, M. (2014). Weight to strength of
topological optimized UAV ribs. Arabian Journal for Science and Engineering, 39(6),
5035-5043
15. Mirik, M., et al. (2006). Using digital image analysis and spectral reflectance data to
quantify damage by greenbug. Computers and electronics in agriculture. 51(1-2), 86-
98
16. Zhang, S., et al. (2016). The accuracy of aerial triangulation products automatically
generated from hyper-spatial resolution digital aerial photography. Remote Sensing
letters. 7(2), 160-169
17. Candon, G.D., et al. (2011). Geo-referencing remote images for precision agriculture
using artificial terrestrial targets. Precision Agriculture. 12(6), 876-891.