Presentation Information
[U06-04]Optical Reflectance–Based Detection of Ganoderma Disease in Oil Palm
*Muhammad Arif Yusuf1, Yukihiro Takahashi2 (1.Indonesian Oil Palm Research Institute, 2.Hokkaido University)
Keywords:
Ganoderma,Remote Sensing,Oil Palm
Basal stem rot (BSR), caused by infection with Ganoderma spp., has emerged as a major constraint to oil palm (Elaeis guineensis Jacq.) production in Indonesia and Malaysia (Susanto et al., 2013). One of the most serious impacts of BSR is its effect on plantation stand density, as severe infections can reduce tree populations to less than 50% of their original density (Hushiarian, Yusof, & Dutse, 2013; Susanto et al., 2013; Priwiratama, Prasetyo, & Susanto, 2014). Such substantial losses pose significant challenges to yield optimization and threaten the long-term sustainability of oil palm cultivation systems. Numerous studies have reported that no fully effective curative treatment for BSR is currently available, with existing management strategies largely focused on prolonging the productive lifespan of infected palms rather than eliminating the disease (Assis et al., 2015; Hushiarian et al., 2013; Ling-Chie et al., 2012; Priwiratama & Susanto, 2014; Susanto et al., 2013). Consequently, early detection plays a pivotal role in disease management. Timely identification of BSR infections enables the implementation of appropriate control measures that can extend the productive period of affected palms and reduce economic losses (Hushiarian, Yusof, & Dutse, 2013; Priwiratama, Prasetyo, & Susanto, 2014).
Remote sensing has increasingly been recognized as a promising approach for large-scale monitoring of BSR in oil palm plantations. By analyzing spectral information from oil palm canopies, remote sensing techniques can detect physiological stress responses associated with infection before visible symptoms appear, making them well suited for monitoring disease occurrence across extensive plantation areas. However, a key limitation of existing studies is their reliance on single-time spectral observations, which characterize disease conditions at a specific moment without capturing the temporal progression of infection (Azmi et al., 2020; Santoso et al., 2018; Santoso, 2020; Santoso, 2023; Kurihara, 2022; Wahyuni et al., 2023). As a result, these approaches provide limited insight into the temporal dynamics of BSR development, which are critical for effective early detection and disease management.
Previous studies on Ganoderma detection using remote sensing have predominantly relied on hyperspectral data or multispectral sensors with a large number of spectral bands, which, although effective, are often constrained by high costs, complex data processing requirements, and limited operational feasibility for routine plantation monitoring (Santoso et al., 2018; Azmi et al., 2020; Santoso, 2020; Wahyuni et al., 2023). Such limitations restrict the scalability and practical implementation of these approaches, particularly in large-scale commercial oil palm plantations.
The use of a 4-band multispectral camera provides a more practical and operationally feasible alternative, while retaining sensitivity to physiological changes induced by Ganoderma infection. Ganoderma-induced stress affects chlorophyll content, leaf structure, and canopy condition, which in turn alters spectral reflectance in the visible and near-infrared regions (Gitelson et al., 1996; Zarco-Tejada et al., 2018). Previous research has demonstrated that a limited number of strategically selected spectral bands—particularly in the green, red, and near-infrared regions—can effectively capture plant stress and disease signals (Calderón et al., 2013; Mahlein, 2016; Zhang et al., 2019). However, the specific application of a simple multispectral configuration based on a 4-band camera for monitoring Ganoderma disease in oil palm remains limited.
This study therefore contributes by demonstrating the potential of a 4-band multispectral imaging approach for detecting and monitoring Ganoderma disease in oil palm.
Remote sensing has increasingly been recognized as a promising approach for large-scale monitoring of BSR in oil palm plantations. By analyzing spectral information from oil palm canopies, remote sensing techniques can detect physiological stress responses associated with infection before visible symptoms appear, making them well suited for monitoring disease occurrence across extensive plantation areas. However, a key limitation of existing studies is their reliance on single-time spectral observations, which characterize disease conditions at a specific moment without capturing the temporal progression of infection (Azmi et al., 2020; Santoso et al., 2018; Santoso, 2020; Santoso, 2023; Kurihara, 2022; Wahyuni et al., 2023). As a result, these approaches provide limited insight into the temporal dynamics of BSR development, which are critical for effective early detection and disease management.
Previous studies on Ganoderma detection using remote sensing have predominantly relied on hyperspectral data or multispectral sensors with a large number of spectral bands, which, although effective, are often constrained by high costs, complex data processing requirements, and limited operational feasibility for routine plantation monitoring (Santoso et al., 2018; Azmi et al., 2020; Santoso, 2020; Wahyuni et al., 2023). Such limitations restrict the scalability and practical implementation of these approaches, particularly in large-scale commercial oil palm plantations.
The use of a 4-band multispectral camera provides a more practical and operationally feasible alternative, while retaining sensitivity to physiological changes induced by Ganoderma infection. Ganoderma-induced stress affects chlorophyll content, leaf structure, and canopy condition, which in turn alters spectral reflectance in the visible and near-infrared regions (Gitelson et al., 1996; Zarco-Tejada et al., 2018). Previous research has demonstrated that a limited number of strategically selected spectral bands—particularly in the green, red, and near-infrared regions—can effectively capture plant stress and disease signals (Calderón et al., 2013; Mahlein, 2016; Zhang et al., 2019). However, the specific application of a simple multispectral configuration based on a 4-band camera for monitoring Ganoderma disease in oil palm remains limited.
This study therefore contributes by demonstrating the potential of a 4-band multispectral imaging approach for detecting and monitoring Ganoderma disease in oil palm.