Article Sidebar

Download
PDF
Statistic
Read Counter : 723 Download : 317

Downloads

Download data is not yet available.

Main Article Content

Abstract

Ganoderma boninense (G.boninense) is the causal agent of basal stem rot (BSR) which significantly reduced the productivity of oil palm plantations in Southeast Asia. At early stage, the disease did not show any physical symptoms that could be seen with naked eyes resulted in detection difficulties. To date, there was no effective detection for this disease, and conventional methods such as manual and laboratory-based required trained specialists as well as time-consuming. Therefore, this study was conducted using hyperspectral remote sensing to investigate the differences in spectral reflectance of young leaf (frond one (F1) of healthy and G. boninense infected oil palm seedlings. The seedlings were inoculated with G. boninense pathogen at five months old. At five months after inoculation, 558 spectral signatures of F1 were extracted from acquired hyperspectral images. Noise removal was done to the extracted spectral signatures to remove outliers in the data. Then, the spectral signatures were averaged and plotted to observe the differences. Differences in reflectance of healthy and G. boninense infected seedlings were seen evidently in the near-infrared (NIR) region. Thus, this study showed evidence that F1 spectral reflectance has the ability to detect early stage of G. boninense infection at oil palm seedlings.

Keywords

Ganoderma boninense BSR disease Hyperspectral imaging Oil palm seedlings Spectral reflectance

Article Details

Creative Commons License

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

How to Cite
Azmi, A. N. ., Bejo, S. K. ., Jahari, M. ., Muharam, F. M. ., & Yule, I. . (2021). Differences Between Healthy and Ganoderma boninense Infected Oil Palm Seedlings Using Spectral Reflectance of Young Leaf Data. Basrah Journal of Agricultural Sciences, 34, 171–179. https://doi.org/10.37077/25200860.2021.34.sp1.17
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. 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
  2. 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
  3. Bravo, C., Moshou, D., West, J., McCartney, A., & Ramon, H. (2003). Early disease detection in wheat fields using spectral reflectance. Biosystems Engineering, 84, 137-145. https://doi.org/10.1016/s1537-5110(02)00269-6
  4. Chang, C. J. (1998). Pathogenicity of aster yellows phytoplasma and Spiroplasma citri on periwinkle. Phytopathology, 88, 1347-1350. https://doi.org/10.1094/PHYTO.1998.88.12.1347
  5. Chappelle, E. W., Kim, M. S., & McMurtrey III, J. E. (1992). Ratio analysis of reflectance spectra (RARS): an algorithm for the remote estimation of the concentrations of chlorophyll a, chlorophyll b, and carotenoids in soybean leaves. Remote Sensing of Environment, 39, 239-247. https://doi.org/10.1016/0034-4257(92)90089-3
  6. Darus, A. & Abu Seman, I. (1993). The Ganoderma selective medium (GSM). In PORIM International Palm Oil Conference. Progress, Prospects Challenges Towards the 21st Century. (Agriculture) September 9-14 Kuala Lumpur, Malaysia (No. L-0218). PORIM. http://palmoilis.mpob.gov.my/images/PORIM%20IS/0008/PORIM%20IS%200008.pdf
  7. Gamon, J. A., & Surfus, J. S. (1999). Assessing leaf pigment content and activity with a reflectometer. The New Phytologist, 143, 105-117. https://doi.org/10.1046/j.1469-8137.1999.00424.x
  8. Gausman, H. W. (1977). Reflectance of leaf components. Remote Sensing of Environment, 6, 1-9. https://doi.org/10.1016/0034-4257(77)90015-3
  9. Gitelson, A. A., Merzlyak, M. N., & Chivkunova, O. B. (2001). Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochemistry and Photobiology, 74, 38-45 https://doi.org/10.1562/0031-8655(2001)074<0038:opaneo>2.0.co;2
  10. Gitelson, A. A., Zur, Y., Chivkunova, O. B., & Merzlyak, M. N. (2002). Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochemistry and Photobiology, 75, 272-281.https://doi.org/10.1562/0031-8655(2002)0750272ACCIPL2.0.CO2
  11. Husin, N. A., Khairunniza–Bejo, S., Abdulllah, A. F., Kassim, M. S. M., Ahmad, D., & Aziz, M. H. A. (2020). Classification of basal stem rot disease in oil palm plantations using terrestrial laser scanning data and machine learning. Agronomy 10, 1624. https://doi.org/10.3390/agronomy10111624
  12. Idris, A. S., & Rafidah, R. (2008). Polyclonal antibody for detection of Ganoderma MPOB Information Series, 405. http://palmoilis.mpob.gov.my/publications/TOT/TT-405.pdf
  13. Idris, A. S., Kushairi, D., Ariffin, D., & Basri, M. W. (2006). Technique for inoculation of oil palm germinated seeds with Ganoderma. MPOB Information Series, 314, 1-4. http://palmoilis.mpob.gov.my/publications/TOT/TT-314.pdf
  14. Idris, A. S., Mazliham, M. S., Loonis, P., & Wahid, M. B. (2010). GanoSken for early detection of Ganoderma infection in oil palm. MPOB Information Series, 442. http://palmoilis.mpob.gov.my/publications/TOT/TT-442.pdf
  15. Idris, A.S., Yamaoka, M., Hayakawa, S., Basri, M. W., Noorhasimah, I., & Ariffin, D., (2003). PCR technique for detection of Ganoderma. MPOB Information Series, 188. http://palmoilis.mpob.gov.my/publications/TOT/tt188.pdf
  16. 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 Conference on Biological and Environmental Science (BIOES 2015), 13-20. https://www.semanticscholar.org/paper/Spectral-based-Analysis-of-Airborne-Hyperspectral-Izzuddin-Idris/827c5439cdeb67d9c4654766cd06a76b9f90e4ee
  17. 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, 532. http://palmoilis.mpob.gov.my/publications/TOT/TT532.pdf
  18. Izzuddin, M. A., Idris, A. S., Nisfariza, M. N., Nordiana, A. A., Shafri, H. Z. M., & Ezzati, B. (2017). The development of spectral indices for early detection of Ganoderma disease in oil palm seedlings. International Journal of Remote Sensing, 38, 6505-6527. https://doi.org/10.1080/01431161.2017.1335908
  19. Kamil, N. N., & Omar, S. F. (2016). Climate variability and its impact on the palm oil industry. Oil Palm Industry Economic Journal, 16, 18-30. http://palmoilis.mpob.gov.my/publications/OPIEJ/opiejv16n1-nadia.pdf
  20. 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.org/10.1016/J.AASPRO.2014.11.023
  21. Kumar, A., Lee, W. S., Ehsani, R. J., Albrigo, L. G., Yang, C., Mangan, R. L. (2012). Citrus greening disease detection using aerial hyperspectral and multispectral imaging techniques. Journal Applied Remote Sensing, 6, 063542. https://doi.org/10.1117/1.JRS.6.063542
  22. Liaghat, S., Mansor, S., Ehsani, R., Shafri, H. Z. M., Meon, S., & Sankaran, S. (2014b). Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm. Computers and Electronics in Agriculture, 101, 48-54. https://doi.org/10.1016/j.compag.2013.12.012
  23. Liaghat, S., Ehsani, R., Mansor, S., Shafri, H. Z., Meon, S., Sankaran, S., & Azam, S. H. (2014a). 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
  24. Lu, J., Ehsani, R., Shi, Y., de Castro, A. I., & Wang, S. (2018). Detection of multi-tomato leaf diseases (late blight, target and bacterial spots) in different stages by using a spectral-based sensor. Scientific Reports, 8, 1-11. https://doi.org/10.1038/s41598-018-21191-6
  25. Malenovský, Z., Ufer, C., Lhotakova, Z., Clevers, J. G. P. W., Schaepman, M. E., Albrechtova, J., & Cudlín, P. (2006). A new hyperspectral index for chlorophyll estimation of a forest canopy: Area under curve normalised to maximal band depth between 650-725 nm. EARSeL eProceedings, 5, 161-172. https://edepot.wur.nl/39655
  26. Moshou, D., Bravo, C., Oberti, R., West, J., Bodria, L., McCartney, A., & Ramon, H. (2005). Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps. Real-Time Imaging, 11, 75-83. https://doi.org/10.1016/j.rti.2005.03.003
  27. Naidu, Y., Siddiqui, Y., Rafii, M. Y., Saud, H. M., & Idris, A. S. (2018). Inoculation of oil palm seedlings in Malaysia with white-rot hymenomycetes: Assessment of pathogenicity and vegetative growth. Crop Protection, 110, 146-154. https://doi.org/10.1016/j.cropro.2018.02.018
  28. 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
  29. Rees, R. W., Flood, J., Hasan, Y., Potter, U., & Cooper, R. M. (2009). Basal stem rot of oil palm (Elaeis guineensis); mode of root infection and lower stem invasion by Ganoderma boninense. Plant Pathology, 58, 982-989. https://doi.org/10.1111/j.1365-3059.2009.02100.x
  30. Sanderson, F. R. (2005). An insight into spore dispersal of Ganoderma boninense on oil palm. Mycopathologia, 159, 139-141. https://doi.org/10.1007/s11046-004-4436-2
  31. Sariah, M., Hussin, M. Z., Miller, R. N. G., & Holderness, M. (1994). Pathogenicity of Ganoderma boninense tested by inoculation of oil palm seedlings. Plant Pathology, 43, 507-510. https://doi.org/10.1111/j.1365-3059.1994.tb01584.x
  32. https://doi.org/10.1080/01431161.2010.519003
  33. Shafri, H. Z. M., & Hamdan, N. (2009). Hyperspectral imagery for mapping disease infection in oil palm plantation using vegetation indices and red edge techniques. American Journal of Applied Sciences, 6, 1031-1035. https://doi.org/10.3844/ajassp.2009.1031.1035
  34. Shafri, H. Z. M., Hamdan, N., & Izzuddin Anuar, M. (2012). Detection of stressed oil palms from an airborne sensor using optimised spectral indices. International Journal of Remote Sensing, 33, 4293-4311. https://doi.org/10.1080/01431161.2011.619208
  35. 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.
  36. Schmidt, K. S., & Skidmore, A. K. (2003). Spectral discrimination of vegetation types in a coastal wetland. Remote Sensing of Environment, 85, 92-108. https://doi.org/10.1016/S0034-4257(02)00196-7
  37. Slaton, M. R., Raymond Hunt Jr, E., & Smith, W. K. (2001). Estimating near‐infrared leaf reflectance from leaf structural characteristics. American Journal of Botany, 88, 278-284. https://doi.org/10.2307/2657019
  38. Shu’ud, M. M., Loonis, P., & Seman, I. A. (2007). Towards automatic recognition and grading of Ganoderma infection pattern using fuzzy systems. International Journal of Medical Health Sciences, 1, 1-6. https://publications.waset.org/5920/towards-automatic-recognition-and-grading-of-ganoderma-infection-pattern-using-fuzzy-systems
  39. Susič, N., Žibrat, U., Širca, S., Strajnar, P., Razinger, J., Knapič, M., & Stare, B. G. (2018). Discrimination between abiotic and biotic drought stress in tomatoes using hyperspectral imaging. Sensors and Actuators B: Chemical, 273, 842-852. https://doi.org/10.1016/j.snb.2018.06.121
  40. Zarco-Tejada, P. J., Miller, J. R., Morales, A., Berjón, A., & Agüera, J. (2004). Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops. Remote Sensing of Environment, 90, 463-476. https://doi.org/10.1016/j.rse.2004.01.017
  41. Zwiggelaar, R. (1998). A review of spectral properties of plants and their potential use for crop/weed discrimination in row-crops. Crop Protection, 17, 189-206. https://doi.org/10.1016/S0261-2194(98)00009-X