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Multi-temporal analysis of terrestrial laser scanning data to detect basal stem rot in oil palm trees

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Abstract

Terrestrial laser scanning technology is an advanced active remote sensing ranging method that is well suited for yielding high-resolution scans of the morphology of a tree, which is an indicator of the health of the plant. The Ganoderma boninense fungus causes basal stem rot (BSR), which threatens the oil palm industry in Malaysia. To date, the current practice of inspection in a plantation is conducted every 6 months. Monitoring the progress with a closer gap is required to comprehend if any changes can be seen earlier than 6 months. Therefore, the objectives of this study were to identify the most suitable parameters of the oil palm trees to detect the BSR disease based on temporal laser scanning data and to identify suitable time frames for monitoring the progress of the symptoms of the disease. Terrestrial laser scanning data was used to analyse changes in the crown and frond metrics of oil palm trees with two different scan durations i.e., 2- and 4-months after the first scan. This spatio-temporal data is important in the precision agriculture field for better oil palm management, to understand the disease development for long-term solutions and also to provide a fast response so that appropriate treatment can be given to the palm as early as possible. Statistical analyses, i.e., the Kruskal–Wallis test with α = 0.05 and the Wilcoxon post-hoc test, were conducted to determine significant differences in the parameters at different points in time. The results show that crown strata number 17 (850 cm from the top) and the crown area were the most suitable parameters to be used. Furthermore, the oil palm trees with BSR can be detected by comparing the 4-month scan or the second 2-month scan. The results have shown that the effect of Ganoderma boninense infection can be differentiated at the later stage. In conclusion, the changes can be measured at 4-months after the first inspection, thus helping to preventing crop losses.

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Funding

The authors would like to acknowledge the Ministry of Higher Education Malaysia and University Putra Malaysia for sponsoring this research under research number LRGS-NANOMITE/ 5526305.

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  1. Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia UPM, Serdang, 43400, Selangor, Malaysia

    Nur A. Husin, Siti Khairunniza-Bejo, Ahmad F. Abdullah, Muhamad S. M. Kassim & Desa Ahmad

  2. Smart Farming Technology Research Centre, Universiti Putra Malaysia UPM, Serdang, 43400, Selangor, Malaysia

    Nur A. Husin, Siti Khairunniza-Bejo & Desa Ahmad

  3. Institute of Plantation Studies, Universiti Putra Malaysia, Serdang, 43400, Selangor, Malaysia

    Siti Khairunniza-Bejo & Muhamad S. M. Kassim

  4. International Institute of Aquaculture and Aquatic Sciences, UPM, 71050, Port Dickson, Negeri Sembilan, Malaysia

    Ahmad F. Abdullah

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Husin, N.A., Khairunniza-Bejo, S., Abdullah, A.F. et al. Multi-temporal analysis of terrestrial laser scanning data to detect basal stem rot in oil palm trees. Precision Agric 23, 101–126 (2022). https://doi.org/10.1007/s11119-021-09829-4

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