Development of a Spectrograph Unmanned Aerial Vehicle for Aerial Imaging of Agricultural Farms

Document Type : Full length article


Assistant Professor, Agricultural Engineering Research Institute, Iran


Todays, the application of new methods for monitoring and online management of agricultural farms is necessary to increase quality and quantity of agricultural products. Remote sensing is one of the technologies that can be used to monitor agricultural farms and natural resources. This technology is capable of detection and prediction of farm changes by application of satellite and aerial images. Satellite images have some limitations for application in agriculture, namely, high cost, low revisit time and low spatial and spectral resolutions.
Thus, recently design of low altitude remote sensing system is considered as a useful tool for ground observations with high spatial resolutions. This system is used to monitor and collect online images for agricultural farms of small spatial farms. So, by the importance of aerial imaging in agricultural farm management and limitations of satellite imagery, the main objective of this research is to develop an unmanned aerial remote sensing system for imaging agricultural farms with high spectral and spatial resolutions.
Materials and Methods
The constructed unmanned aerial vehicle (UAV) is composed of aerial part and ground station. Aerial part is composed of carbon fiber body and arms, 8 brushless DC electric motors, 8 control speeds, control board, PID controller, AHRS system for roll, pitch and yaw angles measurements (with 3 gyroscopes, 3 accelerometers, 3 Magnetometers, one barometer), GPS, camera mount with 2 servo motor for compensation of camera vibration, 3-cell chargeable Li-Po battery (5000mA, 11.1 V) and voltage measurement unit. A multispectral camera (ADC-Micro, Tetracam Company) with 520-920 nm wavelengths and 3.2 Megapixel CMOS sensor in Green, Red and Near Infra Red bands (6-12 V, 2 Gb memory).
Ground station is constructed for conduction and control of aerial part and, indeed, it is connected with operator to aerial part. Ground station is composed of 8 frequency radio control (2.4 GH frequencies), flight monitor software and control software. Total system weight is 2 kg with 78 cm length and width and 29 cm height. The main duty of ground station is following current position and coordination of the UAV, visiting flight parameters and adjustment of primary flight parameters. Sending online imagery of regain to the ground station is carried out by a CCD camera.
To connect aerial and ground parts together, a telemetry system is used. To evaluate the performance of the system, collecting data is carried out in a wheat farm in the Mohammad Shahr, Karaj, Iran.
Results and Discussion
Some factors such as flight endurance, flight maximum preload, maximum and minimum UAV speed, flight altitude, camera mount performance and spatial resolution are investigated. So, imagery are processed, NDVI and supervised classification map is extracted. To evaluate the results of classification, error matrix and overall accuracy is calculated. Based on the results, manuaribilty and stability of the UAV during the flight and also in takeoff and landing positions were satisfactory. The results of experiments showed that the average time endurance of the UAV with installation of GPS, CCD camera, Multispectral camera is 10 minutes. Thus, the results of UAV preload experiments indicated that the maximum preload of the developed UAV is 1 kg. Also, the maximum wind speed for the UAV flight based on the result of this research was 15km/hr. The performance of the system is decreased by increasing wind speed. The optimum UAV speed for collecting qualified imagery is 0-40 km/hr. In higher speed, the quality of imagery will be decreased.
The obtained results from this study are:
- Spatial resolution: based on the results of experiments in the height of 250 m by image dimension (2048*1536), the total covered area in imagery is 2.8 ha. So, time resolution based on the UAV speed and imagery saving file format is 2-5s. So, spatial resolution of imagery in the height of 10m was 3.6 mm/pixel and in the height of 250m the obtained image is 95mm/pixel.
- NDVI map showed higher value in denser regains.
- Supervised classification map (maximum likelihood): the results of experiments showed that the overall accuracy of classification map was 93.99% and Kappa coefficient obtained 0.9. Classification results showed that different classes such as soil, crop canopy, shadow and etc. were separated and recognized completely. High value of the overall accuracy and Kappa coefficient indicates that the UAV is capable of taking good imagery without nose which shows the satisfied performance of control system of the UAV and control system of camera mount in order to keep the UAV in the right situation and to fix camera in the desired position.


Main Subjects

  1. 1.      Berni, J., Zarco-Tejada, P. J., Suarez, L., and Fereres, E. (2009). "Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle". IEEE Transactions on Geoscience and Remote Sensing. 47: 722–738.
  2. 2.      Fukagawa, T., Ishii, K., Noguchi, N., and Terao, H. (2003). "Detecting crop growth by a multispectral imaging sensor". ASAE Paper. No. 033125. St. Joseph, Mich: ASABE.
  3. 3.      Hardin, P.J. and Hardin, T.J. (2010). "Small-scale remotely piloted vehicles in environmental research". Geography Compass. 4: 1297–1311.
  4. 4.      Herwitz, S.R., Johnson, L.F., Dunagan, S.E., Higgins, R.G., Sullivan, D.V., Zheng, J., Lobitz, B.M., Leung, J.G., Gallmeyer, B.A., Aoyagi, M., Slye, R.E. and Brass, J.A. (2004). "Imaging from an unmanned aerial vehicle: Agricultural surveillance and decision support". Computers and Electronics in Agriculture. 44: 49–61.
  5. 5.      Hunt, E.R., Walthall, C.L., Daughtry, C.S. T., Fujikawa, S.J., Yoel, D., Khorrami, F., and Tranchitella, M. (2005). "High resolution multispectral digital photography using unmanned airborne vehicles". In Proc. 20th Biennial Workshop on aerial photography, Videography, and high resolution digital imagery for Resource Assessment. Weslaco. TX: ASPRS: 1536-1539.
  6. Laliberte, A., Rango, A., and Slaughter, A. (2006). "Unmanned Aerial Vehicles (UAVs) for rangeland remote sensing". In Proc. 3rd. Annual symposium research Insights in Semiarid Ecosystems RISE, USDA-ARS Walnut Gulch Experimental Watershed.
  7. Lelong, C.D., Burger, P., Jubelin, G., Roux, B., Labbé, S. and Baret, F. (2008). "Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots". Sensors. 8: 3557-3585.
  8. Moran, M.S., Inoue, Y. and Barnes, E.M. (1997). "Opportunities and limitations for image-based remote sensing in precision crop management". Remote Sensing of Environment. 61: 319–346.
  9. Nebiker, S. Annen, A., Scherrer, M. and Oesch, D. (2008). "A light-weight multispectral sensor for micro UAV: Opportunities for very high resolution airborne remote sensing". The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B1: 1193–1200.
  10. Niethammer, U., James, M.R., Rothmund, S., Travelletti, J. and Joswig, M. (2012). "UAV-based remote sensing of the Super-Sauze landslide: Evaluation and results". Engineering Geology. 128: 2–11.
  11. Primicerio, J., Gennaro, S.F.D., Fiorillo, E., Genesio, L., Lugato, E., Matese, A. and Vaccari, F.P (2012). "A flexible unmanned aerial vehicle for precision agriculture". Precision Agriculture. 13: 517–523.
  12. Rango, A., Laliberte, A., Herrick, J.E., Winters, C., Havstad, K., and Steele, C. and Browning, D. (2009). "Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management". Journal of Applied Remote Sensing. 3 (1): 033542.
  13. Stafford, J.V. (2000). "Implementing precision agriculture in the 21st century". Journal of Agricultural Engineering Research. 76: 267–275.
  14. Sugiura, R., Noguchi, N., Ishii, K. and Terao, H. (2002). "The development of remote sensing system using unmanned helicopter". In Proc. Automation technology for off-road Equipment Conference. Chicago. IL: ASAE.
  15. Swain, K.C., Jayasuriya, H.P.W. and Salokhe, V.M. (2007). "Suitability of low-altitude remote sensing images for estimating nitrogen treatment variations in rice cropping for precision agriculture adoption". Journal of Applied Remote Sensing. 1: 013547.
  16. Valente, J., Sanz, D., Cerro, J.D., Barrientos, A., Miguel, A. and Frutos, A.D. (2012). "Near-optimal overage trajectories for image mosaicing using a mini quad-rotor over irregular-shaped fields". Precision Agriculture. 14: 115–132.
  17. Warren, G. and Metternicht, G. (2005). "Agricultural applications of high-resolution digital multispectral imagery: Evaluating within-field spatial variability of canola (Brassica napus) in Western Australia". Photogrammetric Engineering and Remote Sensing. 71: 595–602.
  18. Xiang, H. and Tian, L. (2011). "Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV)'. Biosystem Engineering. 108 (2): 174-190.
  19. Zhang, J.H., Wang, K., Bailey, J.S. and Wang, R.C. (2006). "Predicting nitrogen status of rice using multispectral data at canopy scale". Pedosphere. 16: 108–117.
  20. Zhu, H., Lan, Y., Wu, W., Hoffmann, W.C., Huang, Y., Xue, X. Liang, J. and Fritz, B. (2010). "Development of a PWM Precision Spraying Controller for Unmanned Aerial Vehicles". Journal of Bionic Engineering. 7 (3): 276–283.


Volume 47, Issue 4 - Serial Number 4
January 2016
Pages 533-546
  • Receive Date: 20 March 2015
  • Revise Date: 10 August 2015
  • Accept Date: 07 September 2015
  • First Publish Date: 22 December 2015