Assessment of Red Tip Disease in Pineapple (Ananas comosus) on Peat Soil Using NDVI
DOI:
https://doi.org/10.53797/agrotech.v2i1.8.2023Keywords:
Pineapple, NDVI, Red tip disease, Greenseeker, Remote sensing, Precision agriculture precisionAbstract
Red tip disease has emerged as a significant constraint in pineapple production on peat soils in Johor, Malaysia. Currently, detection relies on destructive diagnostic techniques such as RNA extraction. This study aimed to evaluate the suitability of a handheld Greenseeker® sensor for detecting red tip disease in pineapple by analyzing the Normalized Difference Vegetation Index (NDVI). Field experiments were conducted at Peninsular Pineapple Sdn. Bhd. (PPSB), Simpang Renggam, involving 7- and 11-month-old SR36 pineapple plots. NDVI readings and disease severity (%DS) were recorded systematically. Regression analysis revealed strong negative correlations between NDVI values and disease severity, with R² values of 0.68 (7-month) and 0.75 (11-month). Laboratory analyses confirmed phytoplasma presence in symptomatic plants using nested PCR. The study concludes that NDVI readings from Greenseeker® offer a reliable, non-destructive method to identify early-stage red tip disease in pineapple cultivation.
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Adams, M. L., Philpot, W. D., & Norvell, W. A. (1999). Yellowness index: An application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation. International Journal of Remote Sensing, 20(18), 3663–3675. https://doi.org/10.1080/014311699211720
Aravind, K. R., Arockia, A. S., & Shanmugam, P. (2022). Remote sensing applications in plant pathology: A review. Frontiers in Plant Science, 13, 864709. https://doi.org/10.3389/fpls.2022.864709
Balasundram, S. K., Husni, M. H. A., & Ahmed, O. H. (2005). Precision pineapple management on tropical peat: I. Spatial variability of yield. Malaysian Journal of Soil Science, 9, 1–10.
Bertaccini, A., & Lee, I. M. (2018). Phytoplasmas: An update. Molecular Plant Pathology, 19(5), 1405–1420. https://doi.org/10.1111/mpp.12661
Caswell, E. P., Sarah, J. L., & Apt, W. J. (1990). Nematode parasites of pineapple. In M. Luc, R. A. Sikora & J. Bridge (Eds.), Plant parasitic nematodes in subtropical and tropical agriculture (pp. 363–376). CABI.
Chappelle, E. W., Wood, F. M., McMurtrey, J. E., & Newcomb, W. W. (1984). Laser-induced fluorescence of green plants. Applied Optics, 23(1), 134–138. https://doi.org/10.1364/AO.23.000134
Doyle, J. J., & Doyle, J. L. (1987). A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochemical Bulletin, 19, 11–15.
FAO. (2021). FAOSTAT – Pineapple production statistics. Food and Agriculture Organization of the United Nations. https://www.fao.org/faostat/
Gausman, H. W. (1974). Leaf reflectance of near-infrared. Photogrammetric Engineering & Remote Sensing, 40(2), 183–191.
Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (2003). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289–298. https://doi.org/10.1016/S0034-4257(96)00072-7
Gundersen, D. E., & Lee, I. M. (1996). Ultrasensitive detection of phytoplasmas by nested-PCR assays using two universal primer pairs. Phytopathologia Mediterranea, 35, 144–151.
Hassan, M. A., Liu, D., & Zhang, Y. (2020). Plant immune responses and growth trade-offs under biotic stresses: From molecular mechanisms to agronomic applications. Plant Cell Reports, 39, 1579–1593. https://doi.org/10.1007/s00299-020-02577-2
Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295–309. https://doi.org/10.1016/0034-4257(88)90106-X
Ismail, B., Nik Masdek, N. H., Zulkefli, M., Malip, M., & Suhaila, A. M. (2006). Factors affecting yield reduction of pineapple on peat. MARDI Research Journal.
Kaminska, M., Sliwa, H., & Labonne, G. (2021). Emerging phytoplasma diseases in horticultural crops. Pathogens, 10(2), 186. https://doi.org/10.3390/pathogens10020186
Lee, I. M., Gundersen, D. E., Hammond, R. W., & Davis, R. E. (1993). Use of mycoplasma-like organism (MLO) group-specific oligonucleotide primers for nested PCR assays to detect MLOs associated with diseases in plants. Phytopathology, 83(8), 834–842. https://doi.org/10.1094/Phyto-83-834
Liu, S., Ma, Y., Zhou, Y., & Wu, C. (2023). Remote sensing applications in agriculture: Recent developments and future trends. Agronomy, 13(3), 734. https://doi.org/10.3390/agronomy13030734
Malaysian Pineapple Industry Board. (2020). Pineapple industry performance report. https://www.mpib.gov.my/
Moore, D. S., McCabe, G. P., & Craig, B. A. (1999). Introduction to the Practice of Statistics. W.H. Freeman.
Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114(4), 358–371. https://doi.org/10.1016/j.biosystemseng.2012.08.009
Nik Masdek, N. H., Ismail, B., Zulkefli, M., & Malip, M. (2005). Pineapple yield decline: The hidden enemy. HRC Technical Report. MARDI.
Pieterse, C. M. J., Van der Does, D., Zamioudis, C., Leon-Reyes, A., & Van Wees, S. C. (2014). Hormonal modulation of plant immunity. Annual Review of Cell and Developmental Biology, 28, 489–521. https://doi.org/10.1146/annurev-cellbio-092910-154055
Pinter, P. J., Hatfield, J. L., Schepers, J. S., Barnes, E. M., Moran, M. S., Daughtry, C. S. T., & Upchurch, D. R. (2003). Remote sensing for crop management. Photogrammetric Engineering & Remote Sensing, 69(6), 647–664.
Qi, J., Cheboouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119–126. https://doi.org/10.1016/0034-4257(94)90134-1
Sellers, P. J. (1985). Canopy reflectance, photosynthesis and transpiration. International Journal of Remote Sensing, 6(8), 1335–1372. https://doi.org/10.1080/01431168508948283
Sether, D. M., & Hu, J. S. (2000). A closterovirus and mealybug exposure are both necessary components for mealybug wilt of pineapple symptom induction. Phytopathology, 90(S71).
Wang, H., Wang, C., Liu, F., Wang, J., & Wang, Y. (2022). Use of NDVI in plant stress detection and precision agriculture: Current status and future perspectives. Remote Sensing, 14(2), 379. https://doi.org/10.3390/rs14020379
Zhang, Y., Zhang, Y., Wang, X., & Liu, Y. (2021). Monitoring crop diseases using handheld NDVI sensors: A review. Sensors, 21(14), 4736. https://doi.org/10.3390/s21144736
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