Article Sidebar

Download
PDF
Statistic
Read Counter : 610 Download : 319

Downloads

Download data is not yet available.

Main Article Content

Abstract

The oil palm is the largest plantation industry in Malaysia. It has been one of the major contributors to the country’s economy and the main pillar of the commodity sectors. For over 40 years, the oil palm industry has faced a lethal and incurable disease, Basal Stem Rot (BSR), which is caused by a type of bracket fungus, Ganoderma boninense. The oil palm physical symptoms infected by BSR disease are appearance of many unopened spears, flattening of crown and smaller crown size. Terrestrial Laser Scanning (TLS, also known as ground-based LiDAR) can be used to provide accurate and precise information on tree morphology with high resolution. This study proposed an image processing technique using the ground input data taken from a TLS. Five parameters were used in the study are number of laser hits in strata 200 cm and 850 cm from the top, namely as C200 and C850, respectively, crown area, frond number and frond angle.  The objectives of this study are to analyse the relationship between the parameters and to study the relationship of the parameters with the levels of BSR disease. Results have shown that all parameters were significant in all levels of healthiness with p-values less than 5%. Frond number and frond angle showed the highest correlation value, which is equal to -0.94. Frond angle is increasing, while frond number and crown area are decreasing concurrently with the severity levels of BSR infection.

Keywords

LiDAR Point cloud Image processing Crown strata Oil palm BSR

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite
Husin, N. A. ., Bejo, S. K., Abdullah, A. F. ., Kassim, M. S. ., & Ahmad, D. . (2021). Relationship of Oil Palm Crown Features Extracted Using Terrestrial Laser Scanning for Basal Stem Rot Disease Classification. Basrah Journal of Agricultural Sciences, 34, 1–10. https://doi.org/10.37077/25200860.2021.34.sp1.1
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Abad-Franch, F., Palomeque, F. S., Aguilar, H. M., & Miles, M. A. (2005). Field ecology of sylvatic Rhodnius populations (Heteroptera, Triatominae): Risk factors for palm tree infestation in western Ecuador. Tropical Medicine and International Health, 10, 1258-1266. https://doi.org/10.1111/j.1365-3156.2005.01511.x
  2. Abdelmonem, A. M., & Rasmy, M. R. (2007). Major diseases of date palm and their control. Communicationes Instituti Forestalis Bohemicae, 23, 9-23.
  3. Abul-Soad, A. A., Mahdi, S. M., & Markhand, G. S. (2015). Date palm status and perspective in Pakistan. In Date palm genetic resources and utilization: pp. 153-205. Springer Netherlands.
  4. Ahmadi, P., Muharam, F. M., Ahmad, K., Mansor, S., & Abu Seman, I. (2017). Early detection of Ganoderma basal stem rot of oil palms using artificial neural network spectral analysis. Plant Disease, 101, 1009-1016. https://doi.org/10.1094/PDIS-12-16-1699-RE
  5. Ayrey, E., Fraver, S., Kershaw Jr., J. A., Kenefic, L. S., Hayes, D., Weiskittel, A. R., & Roth, B. E. (2017). Layer stacking: A novel algorithm for individual forest tree segmentation from LiDAR point clouds. Canadian Journal of Remote Sensing, 43, 16–27. https://doi.org/10.1080/07038992.2017.1252907
  6. Azuan, N. H., Khairunnniza-Bejo, S., Abdullah, A. F., Kassim, M. S. M., & Ahmad, D. (2019). Analysis of changes in oil palm canopy architecture from basal stem rot using terrestrial laser scanner. Plant Disease, 103, 3218-3225. https://doi.org/10.1094/PDIS-10-18-1721-RE
  7. Bejo, S., Abdol-Lajis, G., Abd-Aziz, S., Abu-Seman, I., & Ahamed, T. (2018). Detecting Basal Stem Rot (BSR) disease at oil palm tree using thermal imaging technique. Proceedings of the 14th International Conference on Precision Agriculture, International Society of Precision Agriculture, 24 – 27 June, 2018, Montreal, Quebec, Canada: 8pp.
  8. Bienert, A., Maas, H. G., & Scheller, S. (2006). Analysis of the information content of terrestrial laser scanner point clouds for the automatic determination of forest inventory parameters. In Workshop on 3D Remote Sensing in Forestry, Vienna, Austria, 14, 1-7.
  9. Campos I. L., Veronez, M. R., Wohnrath T. F. M., de Souza, M. K., da Silva, R. M., & Blum Silveira, C. L. (2014). Spectral pattern classification in lidar data for rock identification in outcrops. The Scientific World Journal, 2014, 539029. https://doi.org/10.1155/2014/539029
  10. Colaço, A. F., Trevisan, R. G., Molin, J. P., & Rosell-Polo, J. R. (2017). A method to obtain orange crop geometry information using a mobile terrestrial laser scanner and 3D modeling. Remote Sensing, 9, 763. https://doi.org/10.3390/rs9080763
  11. Djerbi, M. (1998). Diseases of the date palm: present status and future prospects. Journal of Agricultural and Marine Sciences [JAMS], 3,103-114. https://doi.org/10.24200/10.24200/jams.vol26iss1pp1-12
  12. Elliott, M. L., & Broschat, T. K. (2000). Ganoderma Butt Rot of Palms. University of Florida, Florida, Gainesville, 2000, 5. http://vanderbiltlakes.net/PALMS/palmGanodermaButtRotPP10000.pdf
  13. Hoffmeister, D., Waldhoff, G., Korres, W., Curdt, C., & Bareth, G. (2016). Crop height variability detection in a single field by multi-temporal terrestrial laser scanning. Precision agriculture, 17, 296-312. https://doi.org/10.1007/s11119-015-9420-y
  14. Husin, N. A., Khairunniza-Bejo, S., Abdullah, A. F., Kassim, M. S., Ahmad, D. & Azmi, A. N. (2020). Application of Ground-Based LiDAR for Analysing oil palm canopy properties on the occurrence of Basal Stem Rot (BSR) Disease. Scientific reports, 10, 1-16. https://doi.org/10.1038/s41598-020-62275-6
  15. Izzuddin, M. A., Idris, A. S., Nisfariza, N. M., & Ezzati, B. (2015). Spectral based analysis of airborne hyperspectral remote sensing image for detection of Ganoderma disease in oil palm. In Proceedings of 2015 International Conference on Biological and Environmental Science .Phuket, Thailand. 13-20.
  16. Izzuddin, M. A., Idris, A. S., Wahid, O., Nishfariza, M. N. & Shafri, H. Z. M. (2013). Field spectroscopy for detection of Ganoderma disease in oil palm. MPOB Information Series, 532pp.
  17. Kandan, A., Bhaskaran, R. & Samiyappan, R. (2010). Ganoderma–a basal stem rot disease of coconut palm in south Asia and Asia pacific regions. Archives of Phytopathology and Plant Protection, 43, 1445-1449. https://doi.org/10.1080/03235400802536527
  18. Keightley, K. E. & Bawden, G. W. (2010). 3D volumetric modeling of grapevine biomass using Tripod LiDAR. Computers and Electronics in Agriculture, 74, 305-312. https://doi.org/10.1016/j.compag.2010.09.005
  19. Khairunniza-Bejo, S., & Vong, C. N. (2014). Detection of basal stem rot (BSR) infected oil palm tree using laser scanning data. Agriculture and Agricultural Science Procedia, 2, 156–164. https://doi:10.1016/j.aaspro.2014.11.023
  20. Khairunniza-Bejo, S., Yusoff, Y., Yusoff, N. S. N., Seman, I. A., & Anuar, M. I. (2015). Identification of healthy and BSR-infected oil palm trees using color indices. International Journal of Agricultural and Biosystems Engineering, 8, 786-789.
  21. Khairunniza-Bejo, S., Jaleni, M., Husin, M. E., Khosrokhani, M., Muharam, F. M., Seman, I. A., & Anuar, M. I. (2017). Basal Stem Rot (BSR) detection using textural analysis of Unmanned Aerial Vehicle (UAV) image. NanoMite Annual Symposium , 3, 40-45.
  22. Kinge, T. R., & Mih, A.M. (2011). Ganoderma ryvardense sp. nov. associated with basal stem rot (BSR) disease of oil palm in Cameroon. Mycosphere, 2, 179-188.
  23. Lelong, C. C., Roger, J. M., Brégand, S., Dubertret, F., Lanore, M., Sitorus, N., Raharjo, D., & Caliman, J. P. (2010). Evaluation of oil-palm fungal disease infestation with canopy hyperspectral reflectance data. Sensors, 10, 734-747. https://doi.org/10.3390/s100100734
  24. Liaghat, S., Ehsani, R., Mansor, S., Shafri, H. Z., Meon, S., Sankaran, S., & Azam, S. H. (2014). Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms. International Journal of Remote Sensing, 35, 3427-3439. https://doi.org/10.1080/01431161.2014.903353
  25. Lovell, J. L., Jupp, D. L. B., Newnham, G. J., & Culvenor, D. S. (2011). Measuring tree stem diameters using intensity profiles from ground-based scanning lidar from a fixed viewpoint. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 46-55. https://doi.org/10.1016/j.isprsjprs.2010.08.006
  26. Lumme, J., Karjalainen, M., Kaartinen, H., Kukko, A., Hyyppä, J., Hyyppä, H., Jaakola, A., & Kleemola, J. (2008). Terrestrial laser scanning of agricultural crops. International Archives Photogrammetry Remote Sensing and Spatial Information Sciences, 37: 563-566.
  27. Mariau, D., Dery, S. K., Sangare, A., N'Cho Yavo, P., & Philippe, R. (1996). Coconut lethal yellowing in Ghana and planting material tolerance. Plantations Recherche Développement, 3, 105-112. http://www1.biologie.uni-hamburg.de/b-online/library/palms/vpe_coconutly2.htm
  28. MPOC. (2017). Malaysian Palm Oil Industry. Malaysian palm Oil Council. Retrieved on January 2016 from MPOC Website: http://mpoc.org.my/malaysian-palm-oil-industry/
  29. Naher, L., Yusuf, U. K., Ismail, A., Tan, S. G., & Mondal, M. M. A. (2013). Ecological status of 'Ganoderma' and basal stem rot disease of oil palms (Elaeis guineensis Jacq.). Australian Journal of Crop Science, 7, 1723-1727.
  30. Nisfariza, M. N., Shafri, Z. H., Idris, A., Steven, M., Boyd, D., & Mior, M. (2010). Hyperspectral sensing possibilities using continuum removal index in early detection of Ganoderma in oil palm plantation. In: World Engineering Congress 2010, Conference on Geomatics and Geographical Information Science Kuching, Malaysia. 233-239.
  31. Paterson, R. R. M. (2007). Ganoderma disease of oil palm—A white rot perspective necessary for integrated control. Crop Protection, 26, 1369-1376. https://doi.org/10.1016/j.cropro.2006.11.009
  32. Pinruan, U., Rungjindamai, N., Choeyklin, R., Lumyong, S., Hyde, K. D. & Jones, E. G. (2010). Occurrence and diversity of basidiomycetous endophytes from the oil palm, Elaeis guineensis in Thailand. Fungal Diversity, 41, 71-88. https://doi.org/10.1007/s13225-010-0029-1
  33. Santoso, H., Gunawan, T., Jatmiko, R. H., Darmosarkoro, W., & Minasny, B. (2011). Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery. Precision Agriculture, 12, 233-248. https://doi.org/10.1007/s11119-010-9172-7.
  34. Shafri, H. Z., Anuar, M. I., Seman, I. A., & Noor, N. M. (2011). Spectral discrimination of healthy and Ganoderma-infected oil palms from hyperspectral data. International Journal of Remote Sensing. 32, 7111-7129. https://doi.org/10.1080/01431161.2010.519003
  35. Sulaiman, F., Abdullah, N., Gerhauser, H., & Shariff, A. (2011). An outlook of Malaysian energy, oil palm industry and its utilization of wastes as useful resources. Biomass and Bioenergy, 35, 3775-3786. https://doi.org/10.1016/j.biombioe.2011.06.018
  36. Tilly, N., Hoffmeister, D., Cao, Q., Huang, S., Lenz-Wiedemann, V., Miao, Y., & Bareth, G. (2014). Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice. Journal of Applied Remote Sensing, 8, 083671. https://doi.org/10.1117/1.JRS.8.083671
  37. Turner, P. D. (1982). Limitations imposed by disease on oil palm cultivation and yield [esp. fungal diseases]. In International Conference on Oil Palms in Agriculture in the Eighties, Kuala Lumpur (Malaysia), 443-455 pp.
  38. Turner, P. D., & Gillbanks, R. A. (1974). Oil Palm Cultivation and Management. Incorporated Society of Planters, 672 pp.
  39. Verheye, W. (2010). Growth and Production of Oil Palm. In Land use, Land Cover and Soil Sciences. Willy H. Verheye; UNESCO-EOLSS Publishers. Oxford, U. K. 24 pp.

Most read articles by the same author(s)