_Applied_  
_sciences_  
_Article_  
Identification of a Suitable Machine Learning Model for  
Detection of Asymptomatic _Ganoderma boninense_ Infection in  
Oil Palm Seedlings Using Hyperspectral Data  
[PERSON] 1, [PERSON] 1,2,3,*  
Nevertheless, in Southeast Asia, oil palm has been affected by basal stem rot (BSR) disease caused by white-rot fungus identified as _Ganoderma boninense_ (_G. boninense_). BSR is a soil-borne disease that once infected only mature trees; however, the study by [PERSON] [1] reported that seedlings are also susceptible to the infection whereby the symptoms appear earlier and more severe. _G. boninense_ cultivates through uninjured roots and produces enzymes that could degrade the woody tissues, cellulose, lignin layers, and xylem, causing a major disruption in water and nutrients distribution to the top part of the palm [2; 3; 4]. In brief, the earliest symptoms in oil palm seedlings can be detected by the appearance of fungal mass, followed by yellowing and necrosis of older leaves [5]. In severe cases, the infection could cause stunted growth, especially in height, girth and frond count due to the inability to perform photosynthesis. However, the appearance of fungal mass is difficult to examine by naked eyes and can often be overlooked because it may present or may not present before or after the yellowing of leaves [6; 7]. Consequently, Malaysia has reported annual losses of up to RM 1.5 billion due to this disease, making BSR the most economically devastating disease in oil palm plantations. Based on the BSR incident rate, the total area affected in 2020 was estimated to be 443,440 hectares, equivalent to 65.6 million oil palms, which is worrying if preventive measures are not implemented [8]. According to [PERSON] et al. [7], _G. boninense_ infection could cause 80% of the affected trees to die and 25% to 40% yield losses. However, the trees with less than 20% infection can still be treated [9]. Therefore, it is crucial to detect the BSR disease at an early stage to ensure sufficient time for proper treatments, which hence prevent the disease from spreading. Laboratory-based methods are considered reliable for early detection of _G. boninense_. However, these methods involve stem collections that may cause injuries to trees that lead to destruction. Meanwhile, sensor-based methods for BSR detection are widely studied, but these methods are time-consuming and impractical for large-scale plantations. Hyperspectral imaging provides a solution by its ability to cover large areas in a single imaging session. This device has been employed by [PERSON] and [PERSON] [10], [PERSON] and [PERSON] [11], [PERSON] et al. [12], [PERSON] et al. [13], and [PERSON] et al. [14] to detect BSR disease in oil palm plantations. These studies used hyperspectral reflectance data to calculate vegetation indices and optimize spectral indices to differentiate between different healthiness levels of oil palm trees. [PERSON] et al. [12] developed the best approach, which used a combination of red (610.5 nm) and NIR (738 nm) bands to formulate optimized spectral indices. As a result, the ratio of red and NIR bands and the normalized difference vegetation index a (NDVIa) had an overall accuracy of 86%. Based on this research, it can be concluded that hyperspectral imaging is an excellent tool for detecting BSR disease in oil palms. Machine learning techniques have been applied widely in many fields, such as speech recognition [15; 16] and remote sensing land cover detection [17; 18]. In recent years, the agricultural field has also started to take advantage of machine learning capability. For example, in crop yield estimation [19], disease detection [20], weed detection [21], crop quality [22], species recognition [23], animal welfare [24], livestock production [25], water management [26] and soil management [27]. Further, machine learning application in _G. boninense_ detection in mature oil palms was started by [PERSON] et al. [28] in North Sumatra, Indonesia.