A Rapid Technique in Evaluating Tree Health Using Lidar Sensors

  • Authors

    • Siti Farhanah Rosli
    • Fazida Hanim Hashim
    • Thinal Raj
    • Wan Mimi Diyana Wan Zaki
    • Aini Hussain
    2018-08-01
    https://doi.org/10.14419/ijet.v7i3.17.16634
  • LiDAR, autonomous vehicles, tree health, intensity
  • Abstract

    Crop management is one of the key aspects in precision agriculture. On-site crop management includes a scheduled prediction and prescription of fertilization and pesticide application on specific areas of the field. A prediction of the tree health is much needed in order to decide a suitable prescription for the plant.  An autonomous vehicle, equipped with at least one LiDAR (light detection and ranging) sensor, could be used not only for detecting and mapping its surrounding, but to also help evaluate tree health for early distress detection. Currently, farmers have to rely on eyesight to identify trees or plants in distress. Big-scale plantations depend on costly scheduled airborne monitoring routines, which also relies on human vision by scouring through hours of aerial video footage. Both techniques have similar weaknesses in terms of the time it takes to detect a tree in distress and the accuracy of the detection using human vision. The objective of this research is to propose a technique in evaluating tree health using a simple LiDAR sensor that is commonly used in autonomous vehicles. In order to achieve this objective, an evaluation of the different intensity characteristics of tree leaves versus fruits was carried out, both in the lab and in the field. This study has chosen oil palm trees as its subject, as the problem of health monitoring in oil palm plantations is evident. A LiDAR with a 905 nm near-infrared (NIR) laser is used to scan both individual healthy leaves in the lab and different oil palm trees in the field. Since a LiDAR sensor is normally used for ranging, a systematic process was proposed to capture the reflected intensity value of the laser beam that was transmitted to the object. This whole system can be realized using a LiDAR sensor, servo motors, and an Arduino board. Processing software was used to test and store the captured information from the sensor. Later, MATLAB was used to plot the intensity map of the leaves and oil palm tree, classify the range intensity into histograms, and calculate the leaf area index (LAI) for the oil palm trees. From the experimental results, it is found that the reflectance intensity of the leaves shows consistent range values between 155-160 magnitude both in the lab and in the field. From there, three different trees with different number of leaves were scanned and evaluated based on their LAI values. The health of the tree is then predicted, where a healthy tree is estimated to have a higher LAI value. The resulting LAI value is found to correlate with the evaluation using eyesight. This proves that although using a single-wavelength NIR laser beam provided by the LiDAR sensor, as compared to multiple wavelengths of a spectrometer, the difference between the oil palm leaves, fruits and the background noise could be determined. In the future, where multi-wavelength laser LiDAR sensors could be possible, more materials could be characterized. In conclusion, detection, mapping, and materials characterization could be done by an autonomous vehicle utilizing a LiDAR sensor, where tree health could be predicted for crop care management.

     


  • References

    1. [1] Howard, B., 2015. LiDAR and its Use in Agriculture. Agriculture Innovation, Submission 27.

      [2] Gutierrez, R., Gibeaut, J. C., Smyth, R. C., Hepner, T. L., Andrews, J. R. 2001. Precise airborne LiDAR surveying for coastal research and geohazards application. In: International Archives of Photogrammetry and Remote Sensing, Vol. 34-3/W4. Annapolis-MD. pp. 435-452.

      [3] Katzenbeisser, R. 2003. Technical Note on Echo Detection. www.toposys.de/pdfext/Engl/echo-detec3.pdf (Accessed 1. Feb. 2008).

      [4] Reshetyuk, Y. 2006. Investigation of the Influence of Surface Reflectance on the Measurements with the Terrestrial Laser Scanner Leica HDS 3000. Zeitschrift für Geodasie, Geoinformation und Landmanagement 131 (2) : pp. 96-103.

      [5] Kaasalainen, S., Ahokas, E., Hyyppä, J., and Suomalainen, J. 2005. Study of Surface Brightness From Backscattered Laser Intensity: Calibration of Laser Data. IEEE Geoscience and Remote Sensing Letters, Vol. 2, No. 3.

      [6] Kukko A., Kaasalainen S., Litkey P. 2007. Effect of incidence angle on laser scanner intensity and surface data. Applied Optics, Vol. 47, No. 7, March 2008, pp. 986-992.

      [7] Briese, C., Höfle, B., Lehner, H., Wagner, W., Pfennigbauer, M., Ullrich, A. 2008. Calibration of full-waveform airborne laser scanning data for object classification. In: Turner, M.D., Kamerman, G.W. (Eds.) Laser Radar Technology and Applications XIII, SPIE Proceedings Vol. 6950, pp.69500H.

      [8] Lehner, H., Kager, H., Roncat, A., Zlinszky, A., 2011. Consideration of Laser Pulse Fluctuations and Automatic Gain Control in Radiometric Calibration of Airborne Laser Scanning Data. Proceedings of 6th ISPRS Student Consortium and WG VI/5 Summer School.

      [9] Hofton, M.A., Minster, J.B., Blair, J.B., 2000. Decomposition of laser altimeter waveforms. IEEE Transactions on Geoscience and Remote Sensing 38 (4), pp. 1989–1996.

      [10] Persson, Å., Söderman, U., Töpel, J., Ahlberg, S., 2005. Visualization and Analysis of Full-Waveform Airborne Laser Scanner Data. In: Vosselman, G., Brenner, C. (Eds.) Laserscanning 2005. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 36 (Part 3/W19), pp. 109-114.

      [11] Wagner, W., Ullrich, A., Ducic, V., Melzer, T., Studnicka, N., 2006. Gaussian Decomposition and Calibration of a Novel Small-Footprint FullWaveform Digitising Airborne Laser Scanner. ISPRS Journal of Photogrammetry and Remote Sensing, 60 (2), pp. 100-112.

      [12] Song, J., Han, S., Yu, K, and Kim, Y. 2002. Assessing the Possibility of Land-Cover Classification Using LiDAR Intensity Data. ISPRS Communication III, Symposium, Graz, Austria. P. B-259ff.

      [13] Donoghue, D.N.M, Watt, P.J., Cox, N.J., Wilson, J. 2007. Remote Sensing of Species Mixtures in Conifer Plantations using LIDAR height and Intensity data. Remote Sensing of Environment, 110(4): 509-522.

      [14] Langford, J., Niemann, O., Frazer, G., Wulder, M., Nelson, T. 2006. Exploring small footprint LIDAR intensity data in a forested environment. In: Proceedings, IEEE International Conference on Geoscience and Remote Sensing Symposium, Denver CO, 2416-2419.

      [15] Breda, N.J. 2003. Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. Journal of Experimental Botany 54: 2403-2417.

      [16] Jonckheere, I., Fleck, S., Nackaerts, K., Muysa, B., Coppin, P., Weiss, M., and Baret, F. 2004. Review of methods for in situ leaf area index datermination Part I. Theories, sensors and hemispherical photography. Agricultural and Forest Meteorology 121 : 19 – 35.

      [17] Rosli, S.F. Penilaian Pantas Kesihatan Pokok Kelapa Sawit di Ladang. 2017. Tesis Projek Tahun Akhir. UKM.

      [18] Zuhaira Mohd Zulkifli, Fazida Hanim Hashim, Thinal Raj, Aqilah Baseri Huddin. 2018. A Rapid and Non-Destructive Technique in Determining The Ripeness of Oil Palm Fresh Fruit Bunch (FFB). Jurnal Kejuruteraan 30(1) 2018: 93-101.

      [19] Raj, T., & Hashim, F. H., 2016. Synthetic range image simulation of terrestrial LiDAR scanner. Journal of Theoretical and Applied Information Technology, 92(2), 294-304.

  • Downloads

  • How to Cite

    Farhanah Rosli, S., Hanim Hashim, F., Raj, T., Mimi Diyana Wan Zaki, W., & Hussain, A. (2018). A Rapid Technique in Evaluating Tree Health Using Lidar Sensors. International Journal of Engineering & Technology, 7(3.17), 118-122. https://doi.org/10.14419/ijet.v7i3.17.16634

    Received date: 2018-07-31

    Accepted date: 2018-07-31

    Published date: 2018-08-01