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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 [email protected] 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).

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Page 1: Unmanned Aerial Vehicle (UAV) in Agriculture with …Unmanned Aerial Vehicle (UAV) in Agriculture with Crop CAM Finite Element Analysis (FEA) Study Balaji Sethuramasamyraja and Bregan

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

[email protected]

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).

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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.

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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.