_applied_  
_sciences_  
_Article_  
Non-Destructive Detection of Asymptomatic  
_Ganoderma boninense_ Infection of Oil Palm Seedlings Using  
NIR-Hyperspectral Data and Support Vector Machine  
[PERSON] 1,2,3,*, [PERSON] 1, [PERSON] 1 and [PERSON] 1,2  
1 Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia; [EMAIL_ADDRESS] (M.S.S. ); [EMAIL_ADDRESS] (A.N.N.A. ); [EMAIL_ADDRESS] (M.J.)
2 Smart Farming Technology Research Centre, Universit Putra Malaysia, Serdang 43400, Malaysia  
3 Institute of Plantation Studies, Universiti Putra Malaysia, Serdang 43400, Malaysia  
4 Correspondence: [EMAIL_ADDRESS]; Tel. : +60-397694332  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 10:improvements due to better management and more disease resistant varieties not only can prevent the spread of BSR disease in the plantation and subsequent economic losses [6, 7], but also can reduce the pressure for plantation area expansion, which is thus can make a sustainable palm oil production. A comprehensive study to determine the level of resistance or susceptibility of the seedlings to disease is an important aspect to be looked at before any development of a successful breeding program and for the sustainability of this crop, particularly in Southeast Asia [8]. Distinguishing the different levels of plant susceptibility and resistance to the disease is of utmost importance hence, it is crucial to conduct an artificial inoculation of the pathogen at the nursery level to create an early screening test [8]. In common nursery practice, a manual census is used to monitor the progress of the disease development associated with various treatments [9, 10] and is done by humans. Human inspection relies heavily on the visible symptoms of the disease. This method is prone to error due to a lack of experience, subjective judgements, and asymptomatic cases. For early detection of _G. boninense_, laboratory-based methods [11, 12, 13, 14] are considered reliable. However, the procedures entail stem collection, which may result in plant damage and eventual destruction. Other proposed methods used include an electronic nose (e-nose) [15, 16], electrical impedance [17, 18], tomography [19, 20], thermal imaging [21, 22], multispectral imaging [23, 24], spectroscopy or hyperspectral data [25, 26, 27, 28, 29, 30], and a terrestrial laser scanner (TLS) [31, 32, 33, 34, 35]. A comprehensive review of sensors used to detect BSR by [36] has found that each technique has different scores in terms of accuracies and limitations. Based on an in-depth review of the NIR spectroscopy sensor [37], it can be concluded that _G. boninense_ can be detected precisely and sensitively using the near infrared (NIR) spectrum. Furthermore, spectroscopy techniques are found to be more reliable in reactions with organic tissues than chemical and imaging techniques. However, the spectroscopy approach is based on point measurements. It does not incorporate the information of spatial distribution of the measured sample. Contrastingly, hyperspectral sensors collect information as a set of images. It has three-dimensional blocks of data comprising two spatial and one wavelength dimension. Therefore, it can provide both spatial and spectral information simultaneously from a measured sample. According to [38], the NIR-hyperspectral data can clearly differentiate healthy seedlings from the seedlings infected by _G. boninense_, even when there is no appearance of physical symptoms. The infected seedlings demonstrate lower reflectance in the NIR range (750-950 nm) compared to healthy seedlings as a result of xylem destruction, which thus causes a reduction of chlorophyll pigments and a water deficiency. Based on the research work done by [26], the use of SVM with 18 and 35 NIR-hyperspectral wavelengths could provide 100% accuracy of _G.